LTS QA

Automation Testing is a testing technique utilizing automated testing tools to implement tests on multiple platforms. This is considered an efficient software testing method coming with high accuracy and low labor consumption. Still, some obvious and hidden problems do exist behind.

Top 5 automation testing challenges that enterprises have to face:

• High initial investment cost
• High demand for necessary skills
• Complicated maintenance
• Complicated execution
• Difficulties in lab management

This article will dig into these 5 common challenges facing automation testing and solutions to minimize their effects on enterprises.

Top 5 Automation Testing Challenges

1. High initial investment cost

First, let’s take a closer look at the initial investment cost of automation testing. To estimate and calculate the Return of Investment, the first thing you should consider is the possible initial investment cost for an automation testing system, including:

• Cost for human resources
• Cost for automation tools

Cost for human resources

The automation testing process involves the utility of Automated Testing Tools and Automated Testing Engineers. These people are also called Software Development Engineers in Test. 

When comparing the Non-Technical Testers and those with Industrial Knowledge, the second ones are far more expensive. 

Also, the overall In-Demand positions for software testers are plummeting, specifically for automation testers, resulting in higher recruitment competitiveness and higher budgets for talent acquisition.

 

 

The dilemma of human resources lies upon the two forces, which are the Testing Engineers fluent in different coding languages and the Domain Expertise with non-technical knowledge and experience in coding. Whether the testers are onshore or offshore, the cost for those with coding skills is much higher than that of the non-technical testers.

To put it differently, The Non-technical Testers with knowledge of the industry are the trade-off for the Automated Testing Engineers.

Solutions: The problem of high cost for automation test engineers could be handled in two ways:

• Training current employees: This is a budget-friendly way to overcome challenges in automation testing. Still, it often takes many months for an Automated Testing Engineer to really hit it off. 
• Outsourcing automated testing engineers: To avoid spending months on training and coaching, many firms have chosen the solution of outsourcing automated testing engineers.

 

Cost for automation tools

There are two main types of automation testing tools: open-source and commercial testing tools. While the open-source testing frameworks, also called free testing tools (such as Selenium, Katalon, etc.) are free to access, the commercial ones require a payment based on licenses or the number of users.

Still, there are “hidden costs” no matter whether you’re using an open-source testing tool or a licensed one. As for the commercial framework, the payments are obviously the license and development costs. At the same time, free automation testing tools maybe not be enough for your business needs.

Solution: To reduce the cost of automation tools, you should first clarify your requirements and check if free tools can handle your needs. If not, go to a commercial solution that can benefit you the most in the long run.

 

2. Demand for high skills

The myth of automation testing is that it is always wrongly deemed as “simple”, “easy” or “quick”. In fact, the test execution including test design, writing test scripts, test maintenance and technical issue resolution, requires such high automation knowledge and solid grasp of automation tools that the salary range for automation testing engineers is very high.

Typically, automation testing engineers are required to fulfill the job requirements in terms of automation frameworks, prominent programming skills, and solid knowledge of the available automation tools. The strategic skillsets of identifying the appropriate frameworks, applying the right tools, and coordinating the testing process are vital for any automation testing engineer.

Solutions: Companies can weigh the pros and cons of in-house or outsourcing teams for automation testing. These necessary skills above can be acquired through either in-house training or automation testing vendors.

 

3. Complicated maintenance

As automation testing is the hot issue of quality assurance services, its maintenance is imperative for the overall efficiency of the testing process. Throughout the whole testing process, once a test case/script is written, it always necessitates maintenance, which is required every time the software application or features change. 

 

 

The scope of test maintenance varies in accordance with the complication level of the changes themselves. Whether it is a functional or non-functional feature update in the application, viable test cases are to be executed prior to release. As in the comparison of Automation Testing vs. Manual Testing, Automation Testing has different maintainability levels, entailing high programming skills.

Solutions:

• Modular test framework

By applying a modular framework for automated tests, the testing execution is divided into smaller pieces with different functions. Each function of the update is tested, making it easier for automation testing engineers to locate the code that needs updating.

• A separate test for each verification point

There might be a possibility that test developers of automation testing can create numerous verification points. However, the test scripts would hold the crux of complexity, making it difficult for anyone, other than the coder, to edit. With separate test for each verification point, it is easier for the team to update.

• Continuous Integration and Continuous Delivery

Continuous Integration and Continuous Delivery (CI/CD) are the methods in which the minor details/changes are well-attended. With these being applied, the development and testing process is faster and more efficient.

The implementation of CI/CD equals the robust reporting of test scripts and test results. If bugs are to be leaked into other environments, the CI/CD pipeline can help you with the testing process in identifying which part needs updating.

 

4. Complicated execution

During execution, automation scripts are run with input test data. Once execution is finished, detailed test reports will be available. From these reports, appropriate and viable changes and updates can be made.

Automation Testing Execution invokes some difficulties in:

• Test approach selection
• Automation testing tool selection
• Communication and Collaboration

 

High Demand in Test Approach Selection

An appropriate automation test approach plays a key role in the effective result of a project. 

At the management level, you certainly know what and how to make the test approach; however, to make this approach in test automation is another issue. 

• The first difficulty is making the long-run automation process associated with the lifespan of a product. For example, the average cycle of a desktop application is common from 12-18 months to over 15 years. Therefore, the test approach needs to be able to execute the whole process of the software’s life span. 
• Secondly, the test approach has to make sure that when products change or update, it is capable of identifying and keeping up with these changes without human intervention. Taking the example of a mobile application, the approach can’t be “one size fits all” because the user requirement rapidly changes.

Definitely, it is hard to address these difficulties on the test approach, facing the challenge of building an effective long-run-oriented framework at the beginning.

Solution: Identify the following features:

• Testing process
• Testing levels
• Testing types
• Automation tools applicable
• HR allocation with different roles and responsibilities

 

Diverse choices of automation testing tools

One of the automation testing challenges is to select the right testing tool among a variety of comprehensive test tools in the market. There are open-source and commercial tools, and there are various types within each category. Each tool is suitable for particular scenarios, such as Selenium is an open-source tool that requests more programming skills from testers.

Particularly, the right tool has to match many factors such as the long-term orientation of the project, framework, output of the project, the requirement of clients, and the skill of the tester team. So, if you pick the wrong or inappropriate tool, the whole process can be failed from the start. Indeed, open-source tools often require a higher level of coding skill than commercial tools.

Solution:

Our expert testers recommend the following steps to choose tools:

1. Defining a set of tool requirements criteria
2. Reviewing the chosen tools
3. Conducting a trial test with the tools
4. Making the final decision whether you use these tools or not?

 

Barriers in communication and collaboration

In comparison with manual testing and development, automated tests actually require more collaboration. Once the misunderstanding from the start is disregarded or neglected, the process can be messy.

From the beginning, the must-have is good interaction between the delivery team and customer to analyze and understand completely the input and output of the project. 

When it comes to the test strategy, the tester team needs to communicate with project managers about making a plan, scope, and framework. 

The fact that automation testers not only talk with developers for understanding code but also manual testers about test cases, and infrastructure engineers about integration to build up the final product. 

Solution: Establishing a collaborative environment, such as a specific point of contact in each process, clear expectation and the responsibility of members will help everyone to deliver the information fast and conveniently. Plus, active involvement and a transparent framework will develop your unique company culture.

 

5. Difficulties in lab management

A device lab that can match the scope of automation testing has to be a big one. As some of the teams prefer building and maintaining their own device labs, this can be quite extravagant.

For every operating system, there are different versions of browsers and different devices. To fully capture and exploit the utility of this device lab, the up-to-date feature and lab maintenance has to be assured, hence the high cost.

Besides the spiking cost of having your own lab, lab management also poses a great challenge In today’s competitive world, teams need to have the ability to conduct a test at any time.

Your solution needs to provide open access to the lab and equips teams with the right tools to run and perform tests. This ultimately helps you be adaptable and keep pace with the new releases.

 

Solution: Cloud-Based Test Lab

Having a cloud-based lab is key for continuous testing unless there are some special testing requirements/scenarios with IoT, special networking (especially in the Telco space), etc.

 

To sum up, automation testing supports payoffs effectively and is a great method for companies to speed up progress; however, test automation can not completely replace human intelligence. We still need humans to make the orientation in the whole process of automation testing to avoid or reduce the challenges in automation testing.

 

Want to find the solutions for the automation testing challenges? Contact LQA now for FREE consultation with our specialists and experts.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

 

The ever-growing development pace of information technology draws a tremendous need for better speed and flawless execution. So, Automation Testing vs. Manual Testing, which one to go with?

 

As a reflection of this, manual testing is still a vital part of the testing process, non-excludable from the field for some of its specific characteristics. 

Both automation testing and manual testing pose great chances of cost-efficiency and security for your firms. In this article, the three underlying questions of what approach should be applied to your firm for the best outcome will be answered:

• What are the parameters for the comparison between the two?
• What are the pros and cons of automation testing and manual testing?
• Which kind of testing is for which?

 

What is automation testing?

Automation testing is a testing technique utilizing tools and test scripts to automate testing efforts. In other words, specified and customized tools are implemented in the testing process instead of solely manual forces.

Up until now, automated testing is considered a more innovative technique to boost the effectiveness, test coverage, and test execution speed in software testing. With this new “approach”, the testing process is expected to yield more test cases under a shorter amount of time and expand test coverage.

While it does not entirely exclude manual touch within the process, automation testing is a favorable solution for its cost-efficiency and limited human intervention. To put it in other words, automation testing requires manual efforts to make automation testing possible.

 

What is manual testing?

Manual testing, as in its literal meaning, is the technique in which a tester/a QA executes the whole testing process manually, from writing test cases to implementing them.

Every step of a testing process including test design, test report or even UI testing is carried out by a group of personnel, either in-house or outsourced. 

In manual testing, QA analysts carry out tests one-by-one in an individual manner to find bugs, glitches and key feature issues prior to the software application’s launch. As part of this process, test cases and summary error reports are developed without any automation tools.

*Check out:

Why Manual to Automation Testing

6 steps to transition from Manual to Automation testing

 

Magnifying glass for differences between Automation Testing and Manual Testing

Simple as their names are, automation testing and manual testing seem easy to define and identify. However, when looking into the details of many aspects such as test efficiency, test coverage or the types of testing to be applied, it requires a meticulous and strategic understanding of the two.

The differences between automation testing and manual testing can be classified into the following categories:

• Cost
• Human Intervention
• Types of Testing
• Test execution
• Test efficiency
• Test coverage

 

1. Testing cost

For every company, when it comes to testing costs, it requires ubiquitous analysis to weigh in the cost and the benefit to choose a technique for testing.

With the evaluation of potential costs and revenue generated from the project itself, the analysis will determine whether the project needs automation testing or manual testing. As listed in this table, the initial investment, subject of investment and cost-efficiency will be addressed.

Parameters	Automation Testing	Manual Testing
Initial Investment	Automated Testing requires a much larger initial investment to really hit it off. In change for that is the higher ROI yielding in the long run. The cost of automation testing is to cover Automation Testers and open-source automation tools, which can be quite costly.	The initial investment in Manual Testing lies in the cost for human resources and team setup. This may seemingly be economic at first with the cost of just 1/10 of that with automation testing, but in the long-term, the cost can pile up to huge expenses.
Subject of Investment	Investment is resourced for specified and customized tools, as well as automation QA engineers, who expect a much higher salary range when compared to those of manual testing.	Investment is poured into Human Resources. This can be either in-house recruitment or outsourcing, depending on your firm’s request and strategy.
Test volume for cost-efficiency	High-volume regression	Low-volume regression

 

2. Human Resources Involvement

The whole picture of manual testing and automated testing does not simply indulge in the forces that execute the testing, whether it is a human being or a computer. However, there are some universal differences concerning human resources involvement.

Parameters	Automation Testing	Manual Testing
User Interface observation	Automation Testing is basically executed by scripts and codes. Therefore, it cannot score on users’ interaction and opinions upon the software. Matters such as user-friendliness and positive customer experience are out of reach in this case.	The user interface and user experience are put into consideration. This process usually involves a whole team.
Staff’s programming skill requirement	Automation testing entails presets of Most In-Demand programming skills	Manual testing does not necessitate high-profile programming skills or even none.
Salary range	As estimated by Salary.com, the average Automation Test Engineer salary in the United States is approximately 4% higher than that of a regular Software Tester.	The salary range for manual testing is often lower because automated testing requires fluency in different coding languages, which manual testers are incapable of.
Talent availability	It is quite hard for talent acquisition with automation testing engineers.	It is easier for talent acquisitions as the training and coaching for manual testers are easier.

 

3. Testing types

While software testing breaks down into smaller aspects such as performance testing or system testing, Automation Testing or Manual Testing are too general and broad an approach. For each type of testing, we have different approaches, either through an automated one or a manual one. In this article, the following types of testing will be disclosed:

• Performance Testing (Load Test, Stress Test, Spike Test)
• Batch Testing
• Exploratory Testing
• UI Testing
• Adhoc Testing
• Regression Testing 
• Build Verification Testing

Parameters	Automation Testing	Manual Testing
Performance Testing	Performance Testing, including Load Test, Stress Test, Spike Test, is to be tested with Automation Testing.	Manual Testing is not feasible with Performance Testing because of restricted human resources and lack of necessary skills.
Batch Testing	Batch Testing allows multiple test scripts on a nightly basis to be executed.	Batch Testing is not feasible with manual testing.
Exploratory Testing	As exploratory testing takes too much effort to execute, automation testing is impossible	Exploratory testing is for the exploration of the functionalities of the software under the circumstance that no knowledge of the software is required, so it can be done with manual testing
UI Testing	Automated Testing does not involve human interactions, so user interface testing is not feasible.	Human intervention is involved in the manual testing process, so it is proficient to test the user interface with manual testing.
Adhoc Testing	Adhoc testing is performed randomly, so it is definitely not for automation testing.	The core of Adhoc Testing is the testing execution without the instruction of any documents or test design techniques.
Regression Testing	Regression testing means repeated testing of an already tested program. When codes are changed, only automation testing can execute the test in such a short amount of time	Regression testing takes too much effort and too much time to test a changed code or features, so manual test is not the answer for regression testing.
Build Verification Testing	Due to the automation feature, Build Verification Testing is feasible.	It was difficult and time-consuming to execute the Build Verification Testing.

 

4. Test execution

When it comes to testing execution, the expected results are correlated with the actual ones. The answer for “How are automated testing and manual testing carried out?” is also varied, based on the scenario of actual engagement, frameworks, approach, etc.

Parameters	Automation Testing	Manual Testing
Training Value	Automation Testing results are stored in the form of automated unit test cases. It is easy to access and quite straightforward for a newbie developer to understand the codebase.	Manual Testing is limited to training values with no actual documentation of unit test cases.
Engagement	Besides the initial phase with manual testing, automation testing works mostly with tools, hence the accuracy and the interest in testing are secured.	Manual Testing is prone to error, repetitive and tedious, which may cause disinterest for testers.
Approach	Automated Testing is more cost-effective for frequent execution of the same set of test cases.	Manual Testing is more cost-effective for test cases with 1 to 2 test executions
Frameworks	Commercial frameworks, paid tools and open-source tools are often implemented for better outcomes of Automation Testing.	Manual Testing uses checklists, stringent processes or dashboards for test case drafting.
Test Design	Test-Driven Development Design is enforced.	Manual Unit Tests do not involve coding processes.
UI Change	Even the slightest change in the user interface requires modification in Automated Test Scripts	Testers do not encounter any pause as the UI changes.
Access to Test Report	Test execution results are visible to anyone who can log into the automation testing system.	Test execution results are stored in Excel or Word files. Access to these files is restricted and not always available.
Deadlines	Lower risk of missing a deadline.	Higher risk of missing a deadline

 

Also read: Essential QA Metrics to Navigate Software Success

5. Test Efficiency

Test Efficiency is one of the vital factors for a key person to decide whether their firm needs automated testing or manual testing. The fast-paced development of information technology, in general, has yielded more demands in the field of testing, hence skyrocketing the necessity of automation testing implementation.

Regarding test efficiency, automation testing seems to be a more viable and practical approach for a firm with fast execution and sustainability.

Parameters	Automation Testing	Manual Testing
Time and Speed	Automation Testing can execute more test cases in a shorter amount of time	Manual Testing is more time-consuming. It also takes much effort to finish a set of test cases.
Sustainability	Usually, test scripts are written in languages such as JavaScript, Python, or C#. These codes are reusable and quite sustainable for later test script development. Any change can be easily altered with decent skills of coding.	Manual testing does not generate any kind of synchronized documentation for further utility. On the other hand, the skillsets for coding are not necessary.

 

6. Test Coverage

Error detection with Automation Testing is covered more thoroughly. Approaches like reviews, inspections, and walkthroughs are done without leaving anything behind. On the side of manual testing, the numbers of device and operating system permutations are limited. 

 

What are the advantages and disadvantages of automation testing and manual testing?

Automation testing and manual testing both pose great opportunities for the testing industry. For each approach, you have to put many aspects into consideration. In general, automation testing and manual testing have their merits and demerits.

 

Automation Testing pros and cons

Advantages of automation testing

• Reduced repetitive tasks, such as regression tests, testing environments setup, similar test data input
• Better control and transparency of testing activities. Statistics and graphs about test process, performance, and error rates are explicitly indicated
• Decreased test cycle time. Software release frequency speeds up
• Better test coverage

Disadvantages of automation testing

• Extended amount of time for training about automation testing (tools guidance and process)
• The perspective of a real user being separated from the testing process
• Requirement for automation testing tools that can be purchased from third vendors or acquired for free. Each of them has its own benefits and drawbacks
• Poor coverage of the test scope
• Costly test maintenance due to the problem of debugging the test script

 

Manual Testing pros and cons

Advantages of manual testing

• Capability to deal with more complex test cases
• Lower cost   
• Better execution for Ad-hoc testing or exploratory testing
• The visual aspect of the software, such as GUIs (Graphical User Interface) to be covered

Disadvantages of manual testing

• Prone to mistakes
• Unsustainability
• Numerous test cases for a longer time of test execution
• No chance of load testing and performance testing execution

Should you choose automation testing or manual testing?

For each approach of automation testing or manual testing, the question of what to choose for your firm cannot be answered without considering the parametric, the pros and cons of the two.

If your company is a multinational corporation with a vision for large-scale digital transformation, having huge revenue and funds for testing, automation testing is the answer for you. 

Automation testing is sustainable in the long run, enabling your corporation to achieve a higher yield of ROI. It also secures your firm with better test coverage and test efficiency. Automation testing will be the best solution for regression testing and performance testing.

 

If your company seeks a cheaper solution with test case execution under a smaller scope, you should aim at manual testing for a smaller testing cost. User Interface, user experience, exploratory testing, Adhoc testing have to be done with manual testing.

All in all, although automation testing benefits many aspects of the quality assurance process, manual testing is of paramount importance. Please be noted that under the circumstance of frequent changes in test cases, manual testing is compulsory and inseparable from automation testing. The accumulation of the two will generate the most cost-effective approach for your firm.

For the best practices of testing, you should see the automation approach as a chance to perform new ways of working in DevOps, Mobile, and IoT.

 

Want to dig deeper into automation testing vs. manual testing and decide the one for your business? Contact LQA now for a FREE consultation with our specialists and experts.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

In this dynamic era of machine learning, the fuel that powers accurate algorithms and AI breakthroughs is high-quality data. To help you demystify the crucial role of data annotation for machine learning, and master the complete process of data annotation from its foundational principles to advanced techniques, we’ve created this comprehensive guide. Let’s dive in and enhance your machine-learning journey.

Data Annotation for Machine Learning

What is Machine Learning?

Machine learning is embedded in AI and allows machines to perform specific tasks through training. With data AI annotation, it can learn about pretty much everything. Machine learning techniques can be described into four types: Unsupervised learning, Semi-Supervised Learning, Supervised Learning, and Reinforcement learning

• Supervised Learning: Supervised learning learns from a set of labeled data. It is an algorithm that predicts the outcome of new data based on previously known labeled data.
• Unsupervised Learning: In unsupervised machine learning, training is based on unlabeled data. In this algorithm, you don’t know the outcome or the label of the input data.
• Semi-Supervised Learning: The AI will learn from a dataset that is partly labeled. This is the combination of the two types above.
• Reinforcement Learning: Reinforcement learning is the algorithm that helps a system determine its behavior to maximize its benefits. Currently, it is mainly applied to Game Theory, where algorithms need to determine the next move to achieve the highest score.

Although there are four types of techniques, the most frequently used are unsupervised and supervised learning. You can see how unsupervised and supervised learning works according to Booz Allen Hamilton’s description in this picture:

What is Annotated Data?

Data annotation for machine learning is the process of labeling or tagging data to make it understandable and usable for machine learning algorithms. This involves adding metadata, such as categories, tags, or attributes, to raw data, making it easier for algorithms to recognize patterns and learn from the data.

In fact, data annotation, or AI data processing, was once the most unwanted process of implementing AI in real life. Data annotation AI is a crucial step in creating supervised machine-learning models where the algorithm learns from labeled examples to make predictions or classifications.

The Importance of Data Annotation Machine Learning

Data annotation plays a pivotal role in machine learning for several reasons:

• Training Supervised Models: Most machine learning algorithms, especially supervised learning models, require labeled data to learn patterns and make predictions. Without accurate annotations, models cannot generalize well to new, unseen data.
• Quality and Performance: The quality of annotations directly impacts the quality and performance of machine learning models. Inaccurate or inconsistent annotations can lead to incorrect predictions and reduced model effectiveness.
• Algorithm Learning: Data annotation provides the algorithm with labeled examples, helping it understand the relationships between input data and the desired output. This enables the algorithm to learn and generalize from these examples.
• Feature Extraction: Annotations can also involve marking specific features within the data, aiding the algorithm in understanding relevant patterns and relationships.
• Benchmarking and Evaluation: Labeled datasets allow for benchmarking and evaluating the performance of different algorithms or models on standardized tasks.
• Domain Adaptation: Annotations can help adapt models to specific domains or tasks by providing tailored labeled data.
• Research and Development: In research and experimental settings, annotated data serves as a foundation for exploring new algorithms, techniques, and ideas.
• Industry Applications: Data annotation is essential in various industries, including healthcare (medical image analysis), autonomous vehicles (object detection), finance (fraud detection), and more.

Overall, data annotation is a critical step in the machine-learning pipeline that facilitates the creation of accurate, effective, and reliable models capable of performing a wide range of tasks across different domains.

How to Process Data Annotation for Machine Learning?

Step 1: Data Collection

Data collection is the process of gathering and measuring information from countless different sources. To use the data we collect to develop practical artificial intelligence (AI) and machine learning solutions, it must be collected and stored in a way that makes sense for the business problem at hand.

There are several ways to find data. In classification algorithm cases, it is possible to rely on class names to form keywords and to use crawling data from the Internet to find images. Or you can find photos, and videos from social networking sites, satellite images on Google, free collected data from public cameras or cars (Waymo, Tesla), and even you can buy data from third parties (notice the accuracy of data). Some of the standard datasets can be found on free websites like Common Objects in Context (COCO), ImageNet, and Google’s Open Images.

Some common data types are Image, Video, Text, Audio, and 3D sensor data.

• Image data annotation for machine learning (photographs of people, objects, animals, etc.)

Image is perhaps the most common data type in the field of data annotation for machine learning. Since it deals with the most basic type of data there is, it plays an important part in a wide range of applications, namely robotic visions, facial recognition, or any kind of application that has to interpret images.

From the raw datasets provided from multiple sources, it is vital for these to be tagged with metadata that contains identifiers, captions, or keywords.

The significant fields that require enormous effort for data annotation for machine learning are healthcare applications (as in our case study of blood-cell annotation), and autonomous vehicles (as in our case study of traffic lights and sign annotation). With the effective and accurate annotation of images, AI applications can work flawlessly with no intervention from humans.

To train these solutions, metadata must be assigned to the images in the form of identifiers, captions, or keywords. From computer vision systems used by self-driving vehicles and machines that pick and sort produce to healthcare software applications that auto-identify medical conditions, there are many use cases that require high volumes of annotated images. Image annotation increases precision and accuracy by effectively training these systems.

• Video data annotation for machine learning (Recorded tape from CCTV or camera, usually divided into scenes)

When compared with images, video is a more complex form of data that demands a bigger effort to annotate correctly. To put it simply, a video consists of different frames which can be understood as pictures. For example, a one-minute video can have thousands of frames, and to annotate this video, one must invest a lot of time.

One outstanding feature of video annotation in the Artificial Intelligence and Machine Learning model is that it offers great insight into how an object moves and its direction.

A video can also inform whether the object is partially obstructed or not while image annotation is limited to this.

• Text data annotation for machine learning: Different types of documents include numbers and words and they can be in multiple languages.

Algorithms use large amounts of annotated data to train AI models, which is part of a larger data labeling workflow. During the annotation process, a metadata tag is used to mark up the characteristics of a dataset. With text annotation, that data includes tags that highlight criteria such as keywords, phrases, or sentences. In certain applications, text annotation can also include tagging various sentiments in text, such as “angry” or “sarcastic” to teach the machine how to recognize human intent or emotion behind words.

The annotated data, known as training data, is what the machine processes. The goal? Help the machine understand the natural language of humans. This procedure, combined with data pre-processing and annotation, is known as natural language processing, or NLP.

• Audio data annotation for machine learning: They are sound records from people having dissimilar demographics.

As the market is trending with Voice AI Data Annotation for machine learning, LTS Group provides top-notch service in annotating voice data. We have annotators fluent in languages.

All types of sounds recorded as audio files can be annotated with additional keynotes and suitable metadata. The Cogito annotation team is capable of exploring the audio features and annotating the corpus with intelligent audio information. Each word in the audio is carefully listened to by the annotators in order to recognize the speech correctly with our sound annotation service.

The speech in an audio file contains different words and sentences that are meant for the listeners. Making such phrases in the audio files recognizable to machines is possible, by using a special data labeling technique while annotating the audio. In NLP or NLU, machine algorithms for speech recognition need audio linguistic annotation to recognize such audio.

Audio data annotation facilitates various real-life AI applications. A prime example is the application of an AI-powered audio transcription tool that swiftly generates accurate transcripts for podcast episodes within minutes. 

• 3D Sensor data annotation for machine learning: 3D models generated by sensor devices.

No matter what, money is always a factor. 3D-capable sensors greatly vary in build complexity and accordingly – in price, ranging from hundreds to thousands of dollars. Choosing them over the standard camera setup is not cheap, especially given that you would usually need multiple units in order to guarantee a large enough field of view.

 

Low-resolution data annotation for machine learning

In many cases, the data gathered by 3D sensors are nowhere as dense or high-resolution as the one from conventional cameras. In the case of LiDARs, a standard sensor discretizes the vertical space in lines (the number of lines varies), each having a couple of hundred detection points. This produces approximately 1000 times fewer data points than what is contained in a standard HD picture. Furthermore, the further away the object is located, the fewer samples land on it, due to the conical shape of the laser beams’ spread. Thus the difficulty of detecting objects increases exponentially with their distance from the sensor.”

Step 2: Problem Identification

Knowing what problem you are dealing with will help you to decide the techniques you should use with the input data. In computer vision, there are some tasks such as:

• Image classification: Collect and classify the input data by assigning a class label to an image.
• Object detection & localization: Detect and locate the presence of objects in an image and indicate their location with a bounding box, point, line, or polyline.
• Object instance / semantic segmentation: In semantic segmentation, you have to label each pixel with a class of objects (Car, Person, Dog, etc.) and non-objects (Water, Sky, Road, etc.). Polygon and masking tools can be used for object semantic segmentation.

 

Step 3: Data Annotation for Machine Learning

After identifying the problems, now you can process the data labeling accordingly. With the classification task, the labels are the keywords used during finding and crawling data. For instance segmentation task, there should be a label for each pixel of the image. After getting the label, you need to use tools to perform image annotation (i.e. to set labels and metadata for images). The popular annotated data tools can be named Comma Coloring, Annotorious, and LabelMe.

However, this way is manual and time-consuming. A faster alternative is to use algorithms like Polygon-RNN ++ or Deep Extreme Cut. Polygon-RNN ++ takes the object in the image as the input and gives the output as polygon points surrounding the object to create segments, thus making it more convenient to label. The working principle of Deep Extreme Cut is similar to Polygon-RNN ++ but it allows up to 4 polygons.

It is also possible to use the “Transfer Learning” method to label data, by using pre-trained models on large-scale datasets such as ImageNet, and Open Images. Since the pre-trained models have learned many features from millions of different images, their accuracy is fairly high. Based on these models, you can find and label each object in the image. It should be noted that these pre-trained models must be similar to the collected dataset to perform feature extraction or fine-turning.

Types of Annotation Data

Data Annotation for machine learning is the process of labeling the training data sets, which can be images, videos, or audio. Needless to say, AI Annotation is of paramount importance to Machine Learning (ML), as ML algorithms need (quality) annotated data to process.

In our AI training projects, we use different types of annotation. Choosing what type(s) to use mainly depends on what kind of data and annotation tools you are working on.

• Bounding Box: As you can guess, the target object will be framed by a rectangular box. The data labeled using bounding boxes are used in various industries, mostly in automotive vehicle, security, and e-commerce industries.
• Polygon: When it comes to irregular shapes like human bodies, logos, or street signs, to have a more precise outcome, Polygons should be your choice. The boundaries drawn around the objects can give an exact idea about the shape and size, which can help the machine make better predictions.
• Polyline: Polylines usually serve as a solution to reduce the weakness of bounding boxes, which usually contain unnecessary space. It is mainly used to annotate lanes on road images.
• 3D Cuboids: The 3D Cuboids are utilized to measure the volume of objects which can be vehicles, buildings, or furniture.
• Segmentation: Segmentation is similar to polygons but more complicated. While polygons just choose some objects of interest, with segmentation, layers of alike objects are labeled until every pixel of the picture is done, which leads to better results of detection.
• Landmark: Landmark annotation comes in handy for facial and emotional recognition, human pose estimation, and body detection. The applications using data labeled by landmarks can indicate the density of the target object within a specific scene.

Popular Tools of Data Annotation for Machine Learning

In machine learning, data processing, and analysis are extremely important, so I will introduce to you some Tools for annotating data to make the job simpler:

• Labelbox: Labelbox is a widely used platform that supports various data types, such as images, text, and videos. It offers a user-friendly interface, project management features, collaboration tools, and integration with machine learning pipelines.
• Amazon SageMaker Ground Truth: Provided by Amazon Web Services, SageMaker Ground Truth combines human annotation and automated labeling using machine learning. It’s suitable for a range of data types and can be seamlessly integrated into AWS workflows.
• Supervisely: Supervised focuses on computer vision tasks like object detection and image segmentation. It offers pre-built labeling interfaces, collaboration features, and integration with popular deep-learning frameworks.
• VGG Image Annotator (VIA): Developed by the University of Oxford’s Visual Geometry Group, VIA is an open-source tool for image annotation. It’s commonly used for object detection and annotation tasks and supports various annotation types.
• CVAT (Computer Vision Annotation Tool): CVAT is another popular open-source tool, specifically designed for annotating images and videos in the context of computer vision tasks. It provides a collaborative platform for creating bounding boxes, polygons, and more.

When selecting a data annotation for machine learning tool, consider factors like the type of data you’re working with, the complexity of annotation tasks, collaboration requirements, integration with your machine learning workflow, and budget constraints. It’s also a good idea to try out a few tools to determine which one best suits your specific needs.

it is crucial for businesses to consider the top 5 annotation tool features to find the most suitable one for their products: Dataset management, Annotation Methods, Data Quality Control, Workforce Management, and Security.

Who can annotate data?

The data annotators are the ones in charge of labeling the data. There are some ways to allocate them:

In-house Annotating Data

The data scientists and AI researchers in your team are the ones who label data. The advantages of this way are easy to manage and has a high accuracy rate. However, it is such a waste of human resources since data scientists will have to spend much time and effort on a manual, repetitive task.

In fact, many AI projects have failed and been shut down, due to the poor quality of training data and inefficient management.

In order to ensure data labeling quality, you can check out our comprehensive Data annotation best practices. This guide follows the steps in a data annotation project and how to successfully and effectively manage the project:

• Define and plan the annotation project
• Managing timelines
• Creating guidelines and training workforce
• Feedback and changes

Outsourced AI Annotations Data

You can find a third party – a company that provides data annotation services. Although this option will cost less time and effort for your team, you need to ensure that the company commits to providing transparent and accurate data. 

Online Workforce Resources for Data Annotation

Alternatively, you can use online workforce resources like Amazon Mechanical Turk or Crowdflower. These platforms recruit online workers around the world to do data annotation. However, the accuracy and the organization of the dataset are the issues that you need to consider when purchasing this service.

 

The Bottom Line

The data annotation for machine learning guide described here is basic and straightforward. To build machine learning, besides data scientists who will set the infrastructure and scale for complex machine learning tasks, you still need to find data annotators to label the input data. Lotus Quality Assurance provides professional data annotation services in different domains. With our quality review process, we commit to bringing a high-quality and secure service. Contact us for further support!

 

Our Clients Also Ask

 

Recently, outsourcing has not only helped enterprises to cut costs but also become an effective option for strategic management. A prime example of this is test automation outsourcing. It will aid companies in improving the quality of their products, applications, and reducing business risks. LQA’s testing team with over 10 years of experience in test automation and quality assurance, will provide you five tips in test automation outsourcing to maximize the ROI.

 

1. Get to know the engagement models

• Determine the type of test automation outsourcing model. Ask yourself to what extent you want to manage the outsourcing project. If you want to have more control, able to dividing work into smaller projects to mitigate risks then Incremental outsourcing would be the best fit. But, if you want to focus on your core business and leave the testing activity to the third party then Total outsourcing would work.
• Appoint a project manager to supervise the vendor’s performance. Whether you decide to go with the onsite or offshore model, sending a project manager from the client’s side can help assess vendor competencies, set up vendor performance management processes, and track the fulfillment and timeliness of SLA obligations.

 

2. Select an independent and high-proficiency vendor

With the same initial investment, your ROI numbers may vary depending on vendors you work with. Independent quality assurance firms are quickly becoming the favorite vendor since they can provide objectivity and thoroughness. Moreover, with their intensive focus on the testing profession, independent QA vendors can bring out the top quality outcomes with a reasonable investment. Some of the expertise they can aid your company are:

• Compose a throughout test automation strategy
• Design, develop and maintain a flexible test automation architecture
• Advice in choosing a prime test automation framework
• Support automation at both UI and API levels

 

3. Set up a horizontal Collaboration

To make things more beneficial and convenient for both parties, the collaboration with the vendor should be executed at a horizontal level. You can see how we demonstrate this method below:

Your company’s side	Aspect of collaboration	Vendor’s side
CTO or CEO	Strategic alignment, long-term prioritization	CTO or CEO
Software Development / QA Team Leader	Service Level Agreements (SLAs) adjustments, KPI reviews, contract amendments	Account manager
Project Manager	Prioritization and scheduling of QA activities, risk management, process adjustments	Test Automation Manager
Business Analysis, Software Development, Quality Assurance engineers	Daily collaboration	Test Automation engineers

 

 

4. Establish performance measurement metrics

One of the merits of test automation is to reduce ambiguity with easy measurement and metrics. The unit of work is a small deliverable (a test case), so you can easily measure the number of tests automated in a day, per person, tell how much effort is being spent in maintenance, and finally arrive at ROI decisions. The metric establishment should include these activities:

• Setup the service level agreement (SLA) and performance metrics with the vendor: Both parties should work together to figure out a comprehensive SLA before the partnership starts. The agreement should state clearly the responsibilities of the vendor, as well as the KPIs by which the service will be measured.
• Mitigate possible risks of test automation outsourcing: In the cooperation, technical and resource-related risks can result in extra costs and delays in service delivery. For identified risks, managers should develop mitigation and contingency strategies. One specific example is when there is a fluctuating project load, managers can negotiate with the vendor on the possibility of flexible resource allocation within the predefined limits of project load.
• Ensure that SLA terms are followed and met: The project manager should regularly review test result reports. You should pay attention to the combination of such metrics as test coverage and cost per automated test.

 

5. Moving forward to long-term collaboration

Test automation outsourcing may be an effort-intensive undertaking at first, but it will benefit you in the long-term. Automation on long-running projects spans as long as the project does, which typically is many man-years. Therefore the saving and value from automation are sustained over this period, which results in strong ROI. Besides, automated test scripts need minimal intervention and require less frequent test case execution and troubleshooting script errors. It improves manpower utilization, by deploying it in more essential business processes, away from repetitive tasks.

 

When you decide to outsource quality assurance and test automation, there are several elements to consider to achieve the best ROI. With the five steps above, LQA hopes that you can get the desired result with your software testing outsourcing project. If you have any demand in a collaboration with us, you can contact us here.

Singapore, Vietnam, Malaysia and Indonesia are the centers for technology and software development in Southeast Asia. Therefore, software testing engineers are one of the most in-demand position. This report will be helpful for managers who want to figure out the differences of a tester’s salary in these countries.

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1. Software Testing Salary Range

Software testing salary range in Southeast Asia

[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_single_image image=”18310″ img_size=”full” alignment=”center”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]Among the four countries, Singapore has the highest payment range for software testers. On average, testers will receive $5100 per month. The maximum salary that one can be paid is $7980; meanwhile, the minimum is $2660. Malaysia stands in the second position in terms of payment. However, its maximum amount of payment is almost four-time less than Singapore’s. The minimum, average and maximum salary of Malaysian testers consecutively are $690, $1270, and $2030. Of the four representative countries, Vietnam has the lowest salary range. It only costs the manager $330 to $2000 per month to hire a software tester here. The average salary of a Vietnamese is $650, which is three-time less than Singaporean. Nevertheless, the maximum payment for the job is almost equal to Malaysia and higher than in Indonesia. In one month, Indonesian testers can get $360 for the lowest, $720 for the medium, and $1120 for the highest payment.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Software testing salary range in different regions

As seen in the chart above, remote team pricing is broken out into two tiers: Asia and everywhere else. In Asia, the average hourly rate is $24.62/hour, whereas the rest of the world commands higher prices averaged out around $38.67/hour.

A decade ago, there was a 400% difference in pricing from the lowest-priced region to the highest-priced region. Now the range has been cut in half. This ever-narrowing range of prices supports SourceSeek’s guiding principle that the global software market is an efficient one with enough demand to bring consistent pricing that is affected by a small set of characteristics such as location, language skill, proximity, etc.

Outliers are rare. As teams in Eastern Europe slowly set their rates higher and higher, there is enough demand to raise rates in less competitive regions accordingly and still remain competitive. The notable exception is India, where pricing trails the worldwide market due to the sheer volume of supply combined with ongoing reputation issues. There is increasing evidence that China is also beginning to see a similar trend, and will continue to have difficulty entering the global software market.

2. Software Testing Salary Based on Seniority

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Junior Software Tester Salary

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Junior software testers often have less than two years of experience. Within this level, Singaporean testers get paid the most with $3200 per month. It quadruples the salary of a Malaysian tester, who is paid $780. Ranking in third place, Vietnam has a monthly payment of $690, fewer than the second-place $88. The country having the lowest payment for a junior software tester is Indonesia, with $570 a month. It is five-time less than Singapore.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”15px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Senior Software Tester Salary

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If a tester is promoted to a higher level, their salary will be increased. The monthly salary of a QA engineer in Singapore will rise by $1700 to reach $4900. Meanwhile, the salary of a senior tester in Malaysia ranks second place at $1050 per month. Receiving $180 fewer is a Vietnamese tester with a payment of $870. Indonesian tester’s salary is the lowest, which takes employers $770 per month.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”15px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Software Testing Lead Salary

[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column width=”1/2″][vc_single_image image=”18321″ img_size=”full”][/vc_column][vc_column width=”1/2″][vc_column_text]

To hire a software testing lead, an employer has to pay $6400 per month in Singapore. The figures in Vietnam, Malaysia, and Indonesia representatively are $990, $1460, and $1060. It is noticeable that Vietnam is the one who has the lowest salary, where the tester gets a sixth-time fewer than the highest payment.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”15px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

Head of Software Testing Salary

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The salary of the Head of software testing in Singapore is significantly high compared with the other three countries. Tester at this level will be paid $7900 a month, four-time higher than a tester on the same level in Malaysia. Vietnamese and Indonesian testers monthly income are both in the range of $1300, but Indonesian man gets extra $60 which makes Vietnam the lowest pay country for this position.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space height=”30px”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]

3. Salary Based on Education

[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_single_image image=”18322″ img_size=”full” alignment=”center”][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]All four nations show a similar pattern in the chart, which is they will pay a higher salary for tester having higher education level. Besides, with the same degree, testers in Singapore get paid drastically higher than the rest. A tester who holds a certificate or diploma will gain $2660 a month in Singapore, which is eight-time higher than Vietnam and Indonesia, and a fourth-time higher than Malaysia. If the tester gets a bachelor’s degree, he or she will be paid $5100 in Singapore. This number is one-fifth in Malaysia ($1270), $720 in Indonesia, and $650 in Vietnam. Singaporean master’s degree owner will be paid $7980 a month, following by Malaysian and Vietnamese who get $2030 and $2000 representatively. The lowest-paid master’s degree holder is an Indonesian software tester, who gets $1120 per month.[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_column_text]Although there are other countries in South East Asia, the four nations above are representative of the information technology center. Through the article, we showed general guidance of software tester’s salary in Singapore, Vietnam, Malaysia, and Indonesia. All the figures are collected from reliable sources, including Persol Kelly, Michael Page, and First Alliances. Hopefully, the article can be a reference when managers make their decision to hire a software tester. Nevertheless, if managers face difficulties in recruitment, there are still other alternatives such as purchasing software testing outsourcing services.

With a score of 82, Eastern European countries garnered the highest score of any region featured in this report and just edged out East Asia with a score of 80. Eastern Europe has an established reputation for having a mature and robust educational system, and many vendors in the region leverage that reputation to claim that the ‘best developers in the world’ come from Eastern Europe.

Eastern European educational excellence is focused primarily around math and science. The Organization for Economic Co-operation and Development (OECD), which measures 70 countries in reading, math, and science, found that Eastern European countries outperformed other countries featured in this report by an average of 11% in math and 10% in science.

So, while the much-touted claim of ‘best developers in the world’ may be a bit strong, Eastern Europe’s reputation for strong education is well supported by data. While a strong general education is certainly important for a successful IT education, a high score in the UN data doesn’t always result in top IT education, and vice versa.

4. Team composition

Average years of experience is a very informative metric when assessing the maturity of a region as a whole. It takes many years for developers to gain experience and move into management and leadership, making truly senior software engineers difficult to find.

This is exacerbated by brain drain in many countries since many of the most experienced engineers may move on to other more promising regions. Eastern Europe suffered from a bit of brain drain in years past, but for the most part there are adequate opportunities available for software professionals and no need to leave to find work. The presence of so many seasoned professionals also feeds the IT ecosystem, which we’ll look into later in the report.

Lotus Quality Assurance is the first independent software testing company in Vietnam. As a Silver Partner of ISTQB, we provide you a talented team of testing with international experiences. Contact us to be aided with your software testing project.[/vc_column_text][/vc_column][/vc_row]

 

Outsourcing the data labeling services to emerging BPO destinations like Vietnam, China, and India has become a recent trend. However, it is not easy to choose the most suitable data labeling outsourcing vendor among numerous companies. In this article, LQA will walk you through some advices to find the best vendor.

 

1. Prepare a clear project requirement

 

First of all, it is crucial to prepare a clear and detailed requirement which shows all of your expectations toward the final results. You should include the project overview, timeline and budget in your request. A good requirements should include:

– What data types annotators have to work with?
– What kind of annotations need to be done?
– Is it required to have expertise knowledge to label your data?
– The dataset need to be annotated with how much accuracy rate?
– How many files need to be annotated?
– What is the deadline for your project?
– How much can you spend on this project?

 

2. Must-have Criteria to Evaluate the vendors

 

After finalizing your requirements, you should evaluate the vendors with whom you will sign the contract. This stage is crucial since you don’t want to spend plenty of money to receive a poor-labored dataset. We suggest evaluating them based on their experience, quality, efficiency, security, and teammate.

 

Experience

 

While data labeling may often seem like a simple task, it does require great attention to detail and a special set of skills to execute efficiently and accurately on a large scale. You need to gain a solid understanding of how long each vendor has been working specifically in the data annotation space and how much experience their annotators have. To evaluate this, you can ask the vendor some questions about their years of experience, the domain they have worked with, and the annotation types. For example:

How many years of experience in data annotation do the vendors have?
Did they work with a project that requires special domain knowledge before?
Do the vendors provide the type of annotation that matches your requirements?

 

Quality

 

The data scientists often define the quality in datasets for model training by how precisely the labels are placed. However, it is not about labeling correctly one or two times, but it requires consistently accurate labeling. You can figure out the capability of providing high-quality labeled data of the vendors by checking:

The error rates of their previous annotation projects
How accurately placed were the labels
How often did the annotator properly tag each label?

 

Data Quality – 5 Essentials of AI Training Data Labeling Work

 

Efficiency

 

Annotation is more time-consuming than you imagine. For example, a 5-minute video will have an average of 24 frames in one sentence, which made up to 7200 images that need to be labeled. The longer time annotators spend labeling one image, the more hour required to complete the task. To estimate correctly how many man-hours requested to complete your project, you should check with the vendor:

How long did it take to place each label on average?
How long did it take to label each file on average?
How long did it take to execute quality checking on each file?

 

Team

 

Understanding the ability of your vendor annotation team is important as they are the ones who directly execute the project. The vendor should commit to providing you a well-trained team. Moreover, if you want to label text, you need to check if the labeling team can speak the language or not. Besides, confirm with your vendors whether they are ready to scale up or down the annotation team in a short period. Although you may estimate the amount of data to be labeled, your project size still can change over time.

 

Data Annotators: The secret weapon of AI development

 

 

3. Require a pilot project

 

A pilot project is an initial small-scale implementation that is used to prove the viability of a project idea. It enables you to manage the risk of a new project and analyze any deficiencies before substantial resources are committed.

If you ask the vendor to do a pilot project, you will need to choose some sample data from your dataset. You can start with a small amount containing various types of data (10-15 files, depending on the complexity of your dataset).

Remember to provide a detailed guideline for the demo so you can evaluate the vendor correctly. Last but not least, ask them how you can check the progress of the demo test. As a result, you can rate if their quality and performance tracking tools or processes satisfy your requirement or not.

 

We went along with all the set up you need to notice before signing any contract with a data labeling outsourcing vendor. Hopping that with this preparation, you can choose the most decent partner.

If you are shortlisting data labeling vendors, why don’t you include LQA in the list? We have many experiences of labeling data in various fields like healthcare, automotive, and e-commerce. Contact our experts to know more about our experience and previous projects.

The booming of the smartphone has opened the door for global businesses to interact with consumers more effectively and frequently through thousands of applications. Since mobile apps became a significant channel to connect with consumers, executives of firms have spent more effort on enhancing the quality of applications. However, firms have to face many mobile application testing challenges.

According to a report of Capgemini about Quality Assurance and Testing in 2017-18, 47% of the respondents stated that they lack an appropriate testing process or method. Meanwhile, 46% of the companies surveyed don’t know which are the right tools to perform mobile testing. Shortage in testing devices is also a crucial issue that occupies 40%. 

3 Common mobile application testing challenges

1. Lack of efficient testing process

What is the effective testing process for mobile applications in such a high-competitive market like smartphone apps? There are three factors you need to consider, let’s take a look at them below:

Test Strategy

A thorough strategy for your testing project is significant. Some of the aspects that you should plan are Test methodology, Automation testing, and Test environment.

Firstly, when it comes to testing methodology, one of the most favored ones is the Agile approach. In Agile, the development process breaks into repetitive loops, and testing goes in parallel with development.

The second factor you should think about is how to set up the test environment. You can choose from setting up on real mobile devices, on simulators, or clouds. 

The final thing is automation testing. Although test automation can reduce the time and effort to perform repetitive test cases such as regression testing; some of the tests still need to be run manually. One of the efficient ways to apply automation test is when you run one test case on various devices, like in this video: [Demo video] Automation test on 10 mobile devices at the same time

Continuous Testing

Mobile applications are built and updated regularly. As a result, traditional manual testing cannot keep up with the pace of releasing new versions. Continuous testing will run automation tests regularly to get immediate feedback after new updates are released. Moreover, testing apps in parallel with the development process will decrease the risk of failing at the end of the project.

Select Test Types

For mobile application testing, you should execute both functional and non-functional tests. Functional testing includes testing the function of the apps (path testing, boundary values, data lifecycle), application lifecycle, network, and display. Non-functional testing requires testers to perform some special testing, such as: Typical Interrupts Testing, Testing for Power Consumption, Testing for Different Displays, Testing for Device Input Sensors, and Testing for Screen Orientation Change.

2. Choosing from numerous testing tools

What makes mobile testing more complicated is the complexity of mobile testing tools in the market. Each tool has different features that can test a certain type of mobile apps. Companies have to know exactly what they are looking for in the testing tool to choose the one that has appropriate features, such as:

• Fees: Open-source tools and paid tools
• Type of application that the tools can test: Native apps, Web, hybrid apps
• The operation system: iOS, Android, Windows

3. Shortage in testing devices

In 2019, the shipment of smartphones around the world reached 1,375 billion units, in which Android devices accounted for 76% market share and iOS devices took 13%.  Each operating system has various versions, which means mobile apps have to run in numerous environments. This leads to obstacles in setting up mobile testing devices because the testing team cannot access all types of devices available. The solution is you can combine using different test environments such as Real Devices, Emulators / Simulators, and Clouds to perform testing. Each type has its advantages and drawbacks:

Environment	Advantages	Drawbacks
Real devices	Show how the app actually works Can do specific testing for mobile such as Interrupt testing	Not all the target devices are available
Emulators	No need to look for rare devices Simulate hardware and software	Time-consuming to adjust No testing of mobile-specific factors (battery consumption, interrupts, etc.) Not suitable for all types of testing (e.g. UI testing)
Clouds	Unlimited availability of devices	Not always suitable due to security concerns

Nevertheless, the quality assurance team cannot guarantee that if a tested application works well on a given device, it will work 100% on another device. Even though it’s from the same product family, the screen resolution, CPU, Memory, and hardware could be different.

If you want to have more advices about how to improve the efficiency of mobile application testing, you can contact us for a mobile application testing service

When B2C enterprises use mobile applications to attract consumers’ engagement in e-commerce, banking, and marketing; B2B firms use them to manage the company’s operations, tracking the performance of employees, and collaboration with partners. Come with the rising frequency of mobile application usage is the demand for increasing the application quality. If you want to update the latest movement in quality assurance for mobile applications, let’s look at our top 5 mobile testing trends below.

Test Automation

One of the characteristics of mobile applications is that the developers have to release the new version frequently to adapt to the demand of users. Due to this, numerous test cases need to run repeatedly. This is where test automation became an innovative solution. In the World Quality Report conducted by Capgemini, 57% of the companies said that test automation helps to reuse the test case. Meanwhile, 65% of them stated that the test cycle time is reduced.

The greater return on investment in automation not only came from benefits in cost and efficiency but also came from achieving business objectives such as time-to-market. Moreover, it seems like this year CTO concerns more about transparency and security in testing. 69% of them responded that test automation helps them to have better control and transparency of test activities; 62% of respondents think test automation reduces the overall risk.

IoT Testing

The Internet of Things (IoT) allows us to control our assets such as smart home appliances, smart cars, or smartwatches by using a mobile application. Testers need to ensure the quality, security, connectivity, and performance of the application so the interaction between mobile apps and IoT devices is not interrupted. However, executing integration testing is not easy due to IoT devices often having a cloud-based interaction layer developed by a third party. Besides that, testers also concern about the enormous number of test cases needed to run because of the diversity of IoT devices. Testing on emulators, therefore, cannot fulfill the requirement of the QA team. As a result, testing in the cloud is becoming more popular.

Although facing many obstacles, firms are finding ways to adapt to the IoT trend. Many companies consider applying Artificial Intelligence capacity to test thoroughly and also conduct more IoT-experience testing.

Impact of 5G on Mobile Testing

Compared to the previous generation, 5G network has many innovative technologies, which greatly affect how testers conduct mobile testing. When it comes to 5G connectivity, three main technologies change the game: Enhanced mobile Broadband, Ultra-reliable Low Latency Communication, and Massive Machine Type Communication (mMCT)

5G provides greater bandwidth, which means faster data rate and better user experience. With this technology, the 5G network will allow 360-degree video streaming and VR / AR experience. Consequently, testing mobile applications in a high-speed internet office will not be efficient enough. Besides, the latency of data transfer is reduced. It allows faster information receiving within far distance, which has a huge impact on the performance of mobile applications. Meanwhile, mMCT supports connecting with a large number of IoT devices using less power; which leads to changes in battery testing for mobile devices.

Agile and DevOps Approach

Agile and DevOps has become a popular method in continuous testing. They combine the know-how and skill of both testers and developers to support each other with the ultimate goal of fastening the development and deployment of applications. Agile and DevOps approach helps enterprises to find and fix defects more efficiently and release new versions more frequently.

According to the World Quality Report, the Scale Agile Framework (SAFe) and the Dynamic Systems Development Method (DSDM) among Agile methodologies are the most favored by IT firms. From 2015 to 2017, SAFe increased from 31% to 58%; meanwhile, DSDM has grown by 31%. The same report also stated that over 88% of respondents applied DevOps principles in their IT team.

Testing Environment in Cloud

Mobile users reached 1.5 billion at the end of 2020. The mobile devices will have different OS versions, screen resolutions, and data storage. It causes obstacles in mobile testing since firms are not able to buy every kind of mobile phone. As a result, testers tend to use the cloud to test real devices.

Another reason for the increase in using cloud-based testing environments is the virtualization trend in recent years. This will push the demand for cloud and virtual testing tools higher and open more opportunities for firms to provide cloud-based testing services.

Blockchain Testing

Since the booming market of blockchain technology in the IT industry, many have predicted a new and exciting field of better security, higher authenticity and decentralized operations.

Since blockchain technology is rather an immature field of the IT industry, there is more to be explored with this. To ensure a system of decentralized, cost-effective and time-saving activities, blockchain engineers have to employ and adapt to cutting-edge technology and brand new concepts, making them more prone to mistakes.

Under this circumstance, blockchain testing is now an in-demand service that requires a thorough understanding of the blockchain architecture and full test strategy planning.

Blockchain testing should include:

• Smart Contract testing
• API Testing
• Block Testing
• Functional Testing
• Performance Testing
• Security Testing

Big Data testing

With the world’s economy stepping into the recovery phase after the pandemic, many enterprises are trying to revamp their performance with the adoption of big data in their operation and upcoming business plan.

The most prominent approach towards their customers is to collect and min huge data volumes and diverse data types to indicate user behaviors. From these analytical research and collection, one might categorize and analyze their behaviors to find the best matching solution to approach their customers.

To enhance the efficiency of big data in multiple fields with a bigger scope, you would want to implement big data testing, which can help to make improved decisions with accurate data validations. With big data testing facilitates the consistent application of how the datasets perform, market targeting and business manifestation are further streamlined to cope with the ever-changing market.

Cybersecurity Testing

Cybersecurity has always been at the top of the IT industry’s problem with billions of dollars stolen each year. In recent years, many companies have been applying an array of measures to assure cybersecurity within their system and products.

Especially with the fear of data breaches, cybersecurity is gaining its importance in business’ systems and becoming the foremost necessity.

This situation is common across many subdomains of the IT industry, leading to a growth in security testing of from USD 6.1 billion in 2020 to USD 16.9 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 22.3% during the forecast period.

This growth of security testing has brought brighter prospects to the world’s cybersecurity. With the stronger and more effective security measures, cyberattacks, data breaches, malware, etc. will be reduced to their smallest. With this new implementation, digital transformation will be more successful than ever.

Machine Learning and Artificial Intelligence testing

Artificial Intelligence in general and Machine Learning in particular will be the omnipresent technology in the next few years.

As the need for virtual assistants, autonomous cars, etc. are more rival than ever, AI and ML will make a bigger step in the IT industry with top investment priority of CIOs in 2025. As predicted in 2020, the AI and ML market will grow to be about $6-7 billion in America. Technology for behavior prediction, speech recognition is beginning to be adopted and implemented in our daily lives, and it seems to be never enough.

The booming development of AI and ML demands better technology, bigger implementation and breakthrough advances, hence the need for a flawless operation of AI and ML. To achieve this, the most effective and viable solution is AI and ML testing. Since the AI and ML themselves are pretty new in the market, testing for them is quite challenging, yet exciting.

Scriptless automation testing

With Agile and DevOps are trending, requiring developers and testers “low-code” and “no-code” approach. With this approach, developers and testers spend less effort on programming and writing codes, making the software/delivery have a faster time to market.

Although automation testing is on the rise, many testers are struggling with the high-maintenance, script-based testing approach, not to mention the dragging training time. To make it easier and faster for testers to execute tests, many have followed scriptless automation testing.

Since it is scriptless, testers have to utilize multiple tools to acquire desirable test results.

All in all, the trends in 2021 made the QA team face many challenges. How to perform mobile testing with the right strategy remains a hard question. Knowing this constant problem, Lotus QA provides a free consultant for mobile application testing to help you enhance the quality of your product.

In this our new series, 4 things every tester should know, we will take you through the foundational knowledge of testing: Testing levels, Testing Types, Testing Processes and Principles of Testing.

We would like to contribute to the communities the take-away testing videos which are short but essential.

Hence, this series is named “Four Things Every Tester Should Know”, made for:

• Junior testers who are building foundational knowledge in software quality assurance and testing
• Senior testers who wants to revise their knowledge on testing
• Business users who want to have a high-level view of the testing industry

You can watch our video series here, or read the transcription below. Turn on subtitles for English, Japanese, Korean and Vietnamese.

The series will include the following topics:

• Test levels
• Testing types
• Testing processes
• Principles of Testing

https://www.youtube.com/watch?v=1f9Ssis5SuE&list=PLI5JkQdCF-6ic2ceHiitDoIbYrZ1BYttG

Test levels

16 years ago, I came to know about some testing concepts and I think it’s very important.

Today, I want to share you one concept named “Testing Level”. So, let’s get started!

Have you ever heard about V-Model?

As you can see on our picture, there are 4 testing levels ordered from bottom to the top. Those are unit testing, integration testing, system testing, and acceptance testing.

Why do we need to distinguish those testing levels?

Because we can understand the specific objectives of each level you can integrate effectively using them to improve the qualities.

Following the timeline, we start with unit testing.

Unit testing, known as component testing,

is to verify the functional and non-functional behavior of the module in the system.

Normally the programmers do the Unit testing by isolating their module from the rest of the system.

They can do it manually or automatically by using a tool named Junit or Nunit.

The developer will base on detailed design to take the code,

data structure or the database in order to find out

if there are any data flow problems or any incorrect codes.

After finishing unit testing, we go to “Integration testing”

Integration testing is to verify the functional and non-functional interfaces

between components or systems of integration.

So, there are two types of integration.

First one is that component integration testing will be done by the programmer.

And system integration testing might be done by the independent testing team.

So, depending on each type, the input and the output,

and the system under test (SUT) might be differed.

But the objective seems to be same.

We focus on the integration between the component or the system.

The next step is system testing.

System testing focuses on the behavior and the capabilities

of a whole system or whole product as the end-to-end usage.

Independent testing team typically carry out system testing

and they will set up the test environment that is very similar to the production one.

They will produce the testing report which can be used

by stakeholders in order to make release decisions.

The typical defects could be missing the requirements or any incorrect functionalities of the system.

The last level is acceptance testing.

Acceptance testing is similar to the system testing

which will focus on the behavior and also the capabilities

of the whole system or the whole product.

But, it’s different from the system test.

It may produce information to assess

the system’s readiness for deployment and usage by the end-user.

Finding the bug is not the objective of acceptance test

because it will be too late if you find defects at this step.

The acceptance testing is the responsibility of

the customers, business user, product owner and also the operator of the system.

Normally, there are four common forms of the acceptance testing

including the user acceptance testing, the operational acceptance testing,

contractual and regulatory acceptance testing,

and the last one is the alpha and beta testing.

So, those are four important testing levels.

At the beginning of the project,

all stakeholders, including the product owner, the project manager and

also the quality assurance manager should sit together and

plan how to handle those four levels in the projects.

To ensure that, the product and software will meet the business needs and also the user requirement.

Otherwise, the product or the system will be NOT fit to use.

So if you want to see more videos, please subscribe to us.

Also, hit on the bell to receive the notification.

Thank you for watching and see you soon!

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Testing types

A testing types are groups of test activities which are based on specific testing objectives. We could have different aspects to distinguish the different testing types.

For example, following the Quality Characteristic aspect , we could have Functional testing and Non-functional testing. Following the testing method, we could have Whitebox, Greybox, Blackbox testing or Manual/automated testing. Following the testing environment, we could have the alpha, beta and staging testing and so on.

In this video, let’s distinguish just based on the quality characteristic aspect.

So, what is Software Quality Characteristics? Testers need to verify if the software has good quality or not, hence it is necessary to understand how to define the quality.

ISO/IEC 25010 is an international standard which is issued by ISO(international standard organization) for the evaluation of software quality. It defines 8 main (quality) characteristics, namely: Functionality, Performance, Security, and also Compatibility, Reliability, Usability,Maintainability, Portability.

In this video, we will just explain some popular types.

Functional testing & Non-functional testing

First of all, Functional testing. It is a testing type which focused on the completeness, correctness and appropriateness of the software systems.

It can be done manually or automatically supported by many commercial and open source tools.

Meanwhile, Non-functional testing will focus on the “how well” of the system behaves.

Performance testing

The second one is Performance. Performance testing is the process of determining the speed, responsiveness and also the stability of the software prograM, a computer, network, or device under the specific workload.
There are two main performance testing methods: load testing and stress testing.

• Load testing will help you to understand the behavior of a system under a specific load value.
• Stress testing will place a system under the higher-than-expected traffic loads to evaluate ‘how well’ of the system works above its expected capacity limits.

Security testing

And the next testing type is Security testing.

Security testing is to check if the software or the product is secured or not. It checks if the system is vulnerable to attacks or if someone can hack the system or login into the application without the authorization.

Some sources claim that security testing is a functional testing. Because it can be compliant with the old version ‘ ISO 9126’. But following the latest ‘ ISO 25010’ , security testing is a non-functional testing type and it is one of the quality characteristics separately.

Compatibility testing

And the last one I want to introduce today is about Compatibility Testing.

Compatibility Testing is a testing type to check if our software is capable in the different testing environments such as hardware, operating systems, applications, network, mobile or different versions of the software.

Because the compatibility testing will be repeated on the different environment, so it is highly recommended to be automated testing.

From my experience, Testing type is a significant knowledge that every tester should know in order to test correctly. When you are consulting clients and also proposing the quality assurance solution, test experts also should define the important quality characteristics for that specific system and also point out the relevant testing types.

So, that is a take away video about the testing types. It’s quite short but hopefully, it’s helpful.

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Testing processes

Continuing from the previous video about testing level and testing type, today I would like to introduce you another topic about testing process. Why do you need to know about the testing process? Because you will know what you need to do at each step and how to integrate testing into the development process. As you see in our picture, there are 5 steps when you do testing.

Test Planing and Test Control

Test planning is process to output the smart testing way. I often draw out an IMOC into one paper, and then document it later.

• I – the input means Test basic including the software specification, test requirement, software, etc. I plan How and When I can receive input.
• M – M is test Mechanism or how I will do the testing. I should think about the test strategy, schedule, and needed resources in order to do testing. 
• O – Output of testing. Output is a test deliverable. For example, the test result, the test log, and bug report. I will plan of who I need to send the report to, and in which kind of format.
• C – Test constraint. One important test constraint is exit criteria. It is when I can stop testing.

Test control is the process to compare the actual progress and actual reason with the plan at the beginning. You also need to plan how you can control your progress, and how you can control the quality. 

Test Analysis and Test Design

Test Analyzing is the process to analyze all of the test basics such as the test specification, requirement specification, and all additional documentation in order to define the test conditions. Test conditions could be a piece of functionality or anything you want to verify. 

Test Designing is the step to break down the test condition into different test scenarios and cases. It is very important to use different test technique to cover all the possibilities of the test cases.

Test Implementation and Test Execution

In this step, the testers will combine the different test scenarios or test cases into the test procedures following business flows or into the test suite following the purposes of testing. Testers will prepare the test environment and make the automation script if it needs.  After they get ready, the tester will execute the test cases, test suites, test procedures that they made before and log into the testing results. If they find bugs or any incidents, they will report it. 

Evaluation and Reporting

At this step, we will evaluate whether the test implementation is satisfied with the test purpose by comparing test results with the exit criteria at the planning stage. We judge if we need additional testing or not and send reports to all stakeholders.

Test Closure

All of the Test closure activities are done when software is delivered. Tester will check all the deliverable, and archive test ware. Then, they can do any handover or needed training to the maintenance team (organization). And then, they evaluate how the testing went and learn the lessons for future projects.

Those are five steps of the testing process. If you have any suggestion, please comment below. Thank you for watching, and see you soon.
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Principles of Testing

Continuing the series  “Four things every tester needs to know about testing”, today will be the last topic named “7 Principles of testing”. We hope that the series of the video will be helpful for testers, especially freshers. Also through this series of the video, the experienced testers can be reminded about the basic but important knowledge. So, let’s get started! 

Testing can only prove that the software has error

Through the testing, we can find the bug and prove that the software has error. But we can not prove that the software has absolutely no error at all. Even in the absence of errors, we cannot claim that our software has no errors in the future.

Testing the entire pattern is impossible

Testing the entire pattern (combining all conditions of testing entry) is not possible, except for some extremely simple software. Instead of testing the whole system, testers will point out some risky modules to focus on. There are some testing techniques to support us to do that.

Testing should be involved as soon as possible

Testers should be involved in the project as soon as possible to find out the bug early. So, the correction cost will be reduced. Earlier testing, cheaper correction cost.

Defect Clustering – uneven distribution of errors

Most of the errors will be concentrated in some certain modules. It is like the 80-20 principle. So, the smart tester will spend time to analyze the most risky area to focus on.

The pesticides

Have you ever heard about it? The test cases are very similar to pesticides, and can not be used repetitively to eradicate insects during farming. It will over time lead to the insects developing resistance to the pesticide.
If you perform the test case multiple times, it will fail to find the errors. So, it is necessary to review and improve test cases frequently.

Testing depends on context

For different contexts, there will be different testing methods. For example, testing for the banking system will be differed from testing for the sales website; such as different quality characteristics, different testing types and different testing approaches.

“Bug zero” pitfalls

Please do not focus on creating the system without errors, but forget the initial requirements from customers and users. Software testing is not mere finding defects, but it also addresses if the software is fit with the requirement or not.

Those are 7  principles of testing. Please comment if you have any questions. Thank you very much for watching. Have a nice day.

If you like this series, you might also want to take a look at our series on Mobile testing and visit our Youtube channel.

Interested in our Testing Services?

Book a meeting with us now!

Smartphone applications are now capable of acting as sources of entertainment (gaming, music, movies), social media updates and even personal management tool. This means mobile apps are expected to perform much more complicated tasks; leading to focus on several areas in mobile application testing. With this trend, mobile application testing tools are also getting more and more diverse in scope.

Therefore, it is crucial to understand the strengths and weaknesses of each of these tools in order to choose the suitable one for specific tasks.

 

Appium

 

 

 

 

 

 

Appium is an open source testing tool for assessing Android and iOS applications. Developers can test mobile applications, web mobile, and hybrid applications by using this software.

To run the test, Appium uses the WebDriver interface which supports C#, Java, Ruby and many other languages that belong to the WebDriver library. The tester is also able to check initial applications written with the Android and iOS SDKs, mobile web apps, and hybrid apps that contain web views. As a cross-platform tool, it allows developers to reuse the source code between Android and iOS.

 

Robotium

 

 

 

 

 

 

Robotium is an open source tool that allows testing Android applications of all versions; it supports the testing of native and hybrid applications. It uses JavaScript to prepare and execute test scripts. Therefore, Robotium is really popular in the case of automated black box testing for Android applications.

Moreover, it automates many of Android’s operations and creates solid test cases in a minimum of time.

 

Special Features

Multiple Android activities can be handled in parallel.

Robotium can create powerful test scripts in minimal time, without having a deep knowledge of the project.

You can even run test cases on pre-installed applications.

 

Espresso

 

 

 

 

 

 

Espresso is one of the most popular mobile testing frameworks. Created by Google and integrated with Android Studio, this mobile application testing tool is familiar with anyone who develops native Android applications. Like TestComplete, this framework has several options for test script generation, but with Espresso, you can create Android UI tests only.

 

Special Features

A platform-specific solution

Supports all Android instrumentation

Supports manual creation of tests using Kotlin and Java

Has a simple and flexible API

Espresso UI tests can be executed on emulators as well as real devices

 

MonkeyTalk

 

 

 

 

 

 

Next, MonkeyTalk automatically tests the functionality of Android and iOS applications.

Even non-technical people can run tests on this application because it requires no in-depth knowledge of programming and scripting. The scripts of MonkeyTalk are easy to understand, therefore, tester can also generate XML and HTML reports. Besides, it takes screenshots when the failure occurs. In addition, MonkeyTalk supports emulators, network devices and tethered.

 

EarlGrey

 

 

 

 

 

 

EarlGrey is a native iOS UI automation test framework that enables developers to write clear and concise tests, developed and maintained by Google.

With this framework, testers have access to advanced synchronization features. For example, EarlGrey automatically synchronizes with the UI, network requests, and various queues; while still allows the developer to manually implement customized timings.

 

Special Features

Synchronization: From run to run, EarlGrey 2.0 ensures that you will get the same result in your tests, by making sure that the application is idle. These tasks are executed by automatically tracking UI changes, network requests, and various queues. In addition, EarlGrey 2.0 also allows you to manually implement custom timings.

White-box: EarlGrey 2.0 allows you to query the application under test from your tests.

 

Conclusion

Test automation is a complex process, and its adoption requires all the team members to put in a great deal of effort and time. The success of automated tests, however, mainly depend on the mobile testing tools you choose.

While looking for the right tool or framework for writing test scripts, pay attention to its features. Be sure to pick a reliable solution that allows different options for test creation, supports multiple scripting languages and mobile platforms.