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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.

 

For decades, Digital Technology has always been the playground for men, only in recent years that women decided to step up on the game.

Lotus Quality Assurance’s CEO – Mrs. Phung Thanh Xuan after years working as a Technology engineer, is now running her own Global technology firm with nearly 200 staff in five years. We are proud and honor to be leading by a talented, never give in & determined director.

Seeing that many more Vietnamese women are gradually taking on the challenge to break the “Gender bias” barrier in technology is proof showing that our society can change for the better, not only for women but also for men.

Thank you BNews for this interview on International Women Day. Hopefully, this article can inspire many more women to do what they love regardless of the gender stereotype.

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

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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]

We are thrilled to announce that Lotus Quality Assurance company has officially become the ISTQB Silver Partner.

ISTQB is the software testing certification board that offers the world’s most successful scheme for certifying software testers. The ISTQB® Partner Program recognizes organizations with a demonstrated commitment to software testing certifications.

Lotus Quality Assurance has triumphantly passed the essential evaluation, examination and was recognized as the Silver Partner of ISTQB. To hold the title Silver Partner, we have to satisfy ISTQB’s rigorous requirements in the number of certificates as well as the qualifications of testers in our company.

This accomplishment results from our constant training process of high-level testers with global standards. Our team of excellent talents will commit to provide top-quality testing services for every projects.

Becoming ISTQB Silver Partner is our first milestone to grow into the Global Partner in the future. This achievement strengthens our determination to become an international trustworthy QA partner who provides global-standard testing services.

 

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.