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IT Outsourcing for startups – Entrepreneurship can either be as sweet as ripe fruits or as bitter as a cud. Especially under the overwhelming effects of the Covid-19 Pandemic, startups are struggling to beat the odds stacked against them. 

Deprived of a regular and dull nine-to-five job, entrepreneurs have to work around-the-clock to keep the business running. However, this does not ensure success. The essence of one startup to stand out starts from how it is operated. And sometimes, sticking to the regular methods is not exactly the answer.

In other words, it requires entrepreneurs to always think outside of the box alternative solutions to critically solve the root problems, hence boosting the business.

One such solution that is being applied worldwide might be outsourcing, which ironically some startups hesitate to engage in due to the foreseeable additional costs. 

 

 

To comprehensively calculate and estimate the profit margin here, these costs should be seen in the big picture in which the operational costs and HR costs are spiking. For an in-house IT staff, necessary human resources costs or costs for business processes pose a huge overall cost for the company, making it impossible for startups to gain profit.

Instead of in-house staff, more and more startups are leaning towards the idea of outsourcing. This implementation has proven its efficiency, but it can break loose at any time. The point for choosing which to follow lies upon the questions of “Do I want this done in-house or offshore?”, “Which of these options allows for greater growth?”

 

From in-house to IT Outsourcing for Startups 

Shifting from in-house to offshore/outsourcing requires multifaceted consideration. First, startups need to get rid of the knee-jerk reactions toward IT Outsourcing for startups. Lack of experience working through business process outsourcing sometimes leads to the belief that outsourcing, and this should be avoided. 

Typically, Startup owners often think of BPO as something that only large and well-established companies can implement. Facts have proven the other way round. Indeed, companies of all sizes and backgrounds can benefit from this emerging phenomenon. 

A survey from Clutch in 2018 shows that 37% of small businesses outsourced a minimum of one important business process, whereas 52% reported planning to do so the next year. 

While some major players in the field such as GitHub, Google, Microsoft, etc. employ a huge number of outsourced workers, some other companies which started as Startups such as Slack, Skype, Opera, etc. also achieved a lot through BPO.

 

Why outsourcing?

Outsourcing is often deemed as the solution for only large enterprises, in which it can help reduce costs and save time on trivial tasks. Nevertheless, the case of startups can apply the same approach as many are now expanding their businesses, heightening the need for outsourcing many tasks. What exactly can outsourcing do to urge entrepreneurs to go for it?

 

Cut back costs

The most dramatic change BPO companies can pull off is the reduction of operating costs. With costs of insurance, healthcare, travel expenses, rent of facility, etc. unburdened, business leaders now can leave financial resources available to expand and focus on the core business activities. 

 

 

With this being done, the cost reduction can be a part of the strategic growth plan of the business. Besides the common services to be outsourced such as customer support, accounting, HR, IT outsourcing for startups stands out with the highest growth rate due to the ever-expanding scale of the IT industry in general. The services to be outsourced include software testing services, software development services, etc.

 

Halt employee turnover

The puzzle of employee turnover is a chronic problem that has long been in the industry of information technology. A high turnover rate can result in high training costs and low efficiency. To have an outsourcing team taking care of all the training and operating costs for you can really ease out this headache, especially with the competitive prices from different regions. 

 

Experience and expertise from BPO

Startups often step in the game with high confidence, but it should be put within their expertise only. Instead of blindly following something you are not sure of, going for IT outsourcing for startups with specialized individuals and experts in the field is a better option. Hence, the chance of failing by wrongly estimating how well we can do all of the work ourselves is lowered.

Moreover, BPO is experienced in whatever field they are offering you. This also includes certifications, quality assurance, information security, stringent processes, all of which are the things that startups lack.

 

Low rate of burnout

Startups often require high volatility and a fast working pace with huge workloads, which can subsequently lead to burned-out employees. Outsourcing can solve this problem.

IT Outsourcing for startups might be attractive as it is, but please be noted that this is not a one-size-fits-all solution. Before rushing into anything, one must carefully evaluate the pros and cons of IT outsourcing for startups to avoid any kind of miscommunication or misoperation. 

Time zone differences, trustworthiness and credentials are the things you would want to consider when working with BPO. Start small, evaluate everything, and increase IT Outsourcing scale gradually.

 

Are you a startup and want to unburden some costs? Contact us now for full support from experts.

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The world has witnessed unprecedented growth in the information technology market and IT outsourcing trend, which can be seen in almost every aspect of our daily lives. With its share in the “market pie” remaining with a steady rate prior to the Covid-19 pandemic, the year of 2022 will mark a new milestone in the IT field in general and in IT Outsourcing in particular.

Why IT Outsourcing Trend?

IT Outsourcing is a service that has long been on the market with a relatively steady growth rate. As in the IT Services Outsourcing Market Size, Industry Report, 2020-2027 of Grand View Research, the global IT services outsourcing market is projected to grow at USD 520.74 billion in 2019. The annual growth rate (CAGR) from the phase from 2020 to 2027 is expected to be 7.7%.

Taking advantage of the shifting market

For a minor field in Information Technology, IT Outsourcing Service shows potential, but this growth rate was not that dramatic for us to call IT Outsourcing a flagship point.

However, with the world’s economy brought into a sudden and screeching halt due to the pandemic, many giants in business have shifted their focus to virtual/digital engagement with their clients. 

Surging from the uncharted waters, these businesses have proven the viability and possibility of how digital transformation can save a fortune, or perhaps even bring their names to the top of the chain.

Learning from the big names on the market, many other businesses, from big fish to local store owners, all want to apply the technology advances in their operations. To these businesses, digital transformation is the crossing bridge to bring customers and their services closer, especially under the influence of the pandemic in which people prefer virtual interactions.

Take Amazon and Shopify as examples, we can see that the application of e-commerce platforms was spiking in the first half of 2020. These platforms, of course, aim at selling, while their approach is through applications and software. Amazon, or Shopify, has its own in-house development and QA team. But for mid-sized or small-sized companies, they just can’t afford the HR and operation costs. Under this circumstance, the industry is anticipated to witness substantial demand for IT operations so as to allow companies to focus on their core tasks and reduce operating costs.

Parallel to the core tasks, the marginalized tasks also play an increasingly important role in businesses that are planning to foster digital transformation. 

Since the businesses wanting to employ digital transformation have no foundation or background knowledge over information technology, the IT Outsourcing market is progressing owing to the ever-increasing demand for consultancy.

The talking numbers

As the pandemic continues to put a strain on the global economy, many businesses plan to transition to remote work and online customer engagement and order fulfilment. In order to cope with this new approach, they increase spending on clouds, especially software as a service.

Financial cuts in the circumstance of the pandemic are a must, but the reduction in IT Outsourcing has eased from $83 billion in the spring to $31 billion at the end of 2020, signaling the growth in the global IT spending.

Worldwide IT spending is projected to total $4.5 trillion in 2022, according to Gartner’s forecast, growing by 3% compared to 2021, despite that people tend to cutback spending on PCs, tablets and printers by consumers 

As in the first phases of the pandemic in 2020, every aspect of the IT service was declining, but it began to take the initial steps for a huge growth in the years coming. For example, after contracting 4.6% in 2020 to $490 billion, worldwide IT spending on consulting and implementation services are predicted to experience a 4.5% CAGR through 2024. While worldwide spending on IT-centric managed services, infrastructure, and application support, which decreased 1.1% in 2020 to $475 billion, will see a CAGR of 5.3% through 2024. 

What companies want from their IT outsourcing providers

Pre-pandemic, the main focus for IT outsourcing providers was narrowly on specific services such as helpdesk, infrastructure, storage, network monitoring and network management. 

Post-pandemic, with the preferred solutions for digital transformation stay on top in almost every business, the IT outsourcing services are demanded to leverage and innovate to cope with the urgent needs for a wider range of requirements.

Subsequently, the expected outcome for this whole IT outsourcing service is cost avoidance. To achieve this, IT outsourcing providers are to fulfil the needs for:

1. AI and Automation

The employment of 4.0 Technology is developing with the pace that we’ve never seen before, leading to an upsurge in the need for human resources and infrastructure. Thousands of applications pilot every day, each with many features that require timid, tedious work of coding, testing and maintenance.

To this point, businesses who want to be ahead of the curve have to take advantage of being the pioneer, meaning they have to be the fastest and the most productive. Instead of the traditional way of expanding the team with experts in the field (which can be quite costly), many of the business owners decide to go for a cost-effective approach, AI and automation.

The fascinating idea of AI – a non-human machine that can interact with people is on the rise. But the real benefit of this is to reduce HR and operational costs. For example, before assigning a customer to a human customer service officer, the system has a chatbot to answer and interact with them. Only when the bot cannot figure out the requirement and how to fulfil it do they transfer the customer to a CS. With the bot work regardless of time, the business can save a fortune on the cost for a CS team.

2. Growth of the Cloud Services

On-premise storage for data management has shown weakness and limitations, hence the IT outsourcing trend in shifting to cloud services. 

Alongside the current worth of cloud computing reaching the hallmark of $180 billion worldwide is the market growth by 24% of PaaS, SaaS, and IaaS sections. In two years’ time, cloud computing service is predicted to soar to over $623.3 billion. 

One of the reasons why cloud computing is on the rise is the better protection of data. Moreover, it also ensures faster data operations and the ability to modernize business processes.

3. 5G

5G wireless technology is meant to deliver higher multi-Gbps peak data speeds, ultra low latency, more reliability, massive network capacity, increased availability, and a more uniform user experience to more users. Higher performance and improved efficiency empower new user experiences and connect new industries. – Qualcomm

With the employment of 5G in almost every aspect of the IT world, it speeds up the adoption of reliability, low latency and larger network capacity. Alongside its emerging deployment in major aspects such as medtech or Internet of Things, 5G also plays an important role in the development of AI implementation.

For example, as the Covid-19 pandemic took its toll on the world, some 5G-based applications have already made their way into medtech, especially in the adoption of telehealth and remote monitoring. All of the wireless technology, powered by 5G, have benefited the healthcare staff with utmost convenience.

For the part of AI implementation, 5G is pervasive in domains such as autonomous driving, virtual reality and augmented reality. With higher connection density and the ability to handle an immense number of connected devices at the same time, 5G comes to the forefront as the pioneering factor for both cost avoidance and service enhancement.

4. Cybersecurity

There’s no denying that information technology advances are developing with upsoar rate, resulting in the ever-growing number of service end-users. Larger number of users equals larger threat of cybersecurity. 

To have a screw loose in the cybersecurity is to bring threats to the system, but to recruit a full-stack IT security engineer is no easy task. Instead of having an in-house staff who works full-time, businesses are leaning towards IT outsourcing. They often need:

• Monitor your environment 24/7
• Thorough security staff training
• Security strategy
• Security architecture

One report from Allied Market Research estimates the market to reach nearly $41 billion by 2022, based on a 16.6% compound annual growth rate between 2016 and 2022.

5. Remote Work Statistics

According to Weforum, “The number of days US employees spend working from home increased from 1.58 per week in January 2021 to 2.37 in June 2022”, as the result of Covid-19. The IT sector, among many other sectors, has witnessed the dramatic shift to remote work, marking a new IT Outsourcing trend in the IT outsourcing market.

Working remotely is not new, especially under the specific traits of how IT staff can work. However, the rate is increasing with soaring popularity.

A report by Avasant shows that middle-sized tech companies have been the largest contributors to the growth of the IT outsourcing industry in 2020.

It’s also declared that the average outsourcing for midsize companies went from 9.1% to 11.8%. So while some tech businesses increased their IT budgets on the brink of the pandemic, the rest continued to work with their nearshore and offshore IT outsourcing partners to reduce development costs.

Delve deeper into other technology trends and industry movement.

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Automated Data Labeling is a new feature that is currently being constantly mentioned among Data annotation trends, and some even deem it the solution for the time-consuming and resource-consuming casual manual annotation.

As the Manual Data Labeling – aka Manual Data Annotation takes hours to annotate one dataset, the Automated data labeling technology now proposes a simpler, faster and more advanced way of processing data, through the use of AI itself.

 

How we normally handle dataset

The most common and simplest approach to data labeling is, of course, a fully manual one. A human user is presented with a series of raw, unlabeled data (such as images or videos), and is tasked with labeling it according to a set of rules.

For example, when processing image data for machine learning, the most common types of annotations are classification tags, bounding boxes, polygon segmentation, and key points.

 

 

Classification tags, which are the easiest and cheapest annotation, may take as little as a few seconds whereas fine-grained polygon segmentation could take a few minutes per each instance of objects.

In order to calculate the impact of AI automation on data labeling times, let’s assume that it takes a user 10 seconds to draw a bounding box around an object, and select the object class from a given list.

In this case, provided with a typical dataset with 100,000 images and 5 objects per image, annotators would have to spend 1,500 man-hours to complete the annotation process. This eventually would cost approximately $10,000 just for data labeling. 

The price of $10.000 is only for data labeling. For annotation project managers, AI data processing takes more than that. To ensure the high quality of the training data, they are compelled to add other layers of quality control and quality assurance. This helps manually verify and review each piece of labeled data, but it would be very costly. Moreover, the quality control and quality assurance staff must be trained of the sample output so that they understand what is required in the outcome of the annotation projects, thereby increasing the labeling costs by about 10%.

 

 

Some annotation project managers might choose consensus-based quality control. By implementing this method, the whole annotation project goes through multiple annotations. The same piece of data is annotated multiple times, and the results are consolidated and compared for quality control purposes. With this method, the amount of time and money is proportional to the number of annotators working on the same task. Simply put, if you had three users label the same image three times, you would have to pay for all 3 annotations. 

All this is to emphasize that, the two most expensive steps in data labeling are:

• The data labeling itself
• Reviewing and verifying it for quality control. 

 

Looking at all the huge costs that it would take in an annotation project, many business leaders have turned into a less time-consuming and tedious solution, which is the auto annotation tool technology.

Thankfully, with the latest technologies in artificial intelligence and machine learning, automated data labeling, or auto annotation, is usable now. However, to create an effective and well-rounded auto annotation tool now, it even requires more training data and human input for correcting errors induced by the AI. Therefore, anyone has the naive attempt to entirely apply auto annotation tools, they have to be cognizant of the truth that the tools are not the one-size-fits-all solution.

 

The advantages of Automated Data Labeling

Automated data labeling is quite a new term in the field, but the technology advancement implementing and making it happen is developing with high speed, shown in the large number of tools on the market now. So what are auto data labeling and its benefits?

 

What’s automated data labeling?

Automatic labeling is a feature found in data annotation tools that apply artificial intelligence (AI) to enrich, annotate, or label a dataset. Tools with this feature augment the work of humans in the loop to save time and money on data labeling for machine learning.

 

 

Most tools allow you to load pre-annotated data into the tool. More advanced tools, which are evolving into platforms (e.g., tool plus Software Development Kit or SDK), allow you to leverage AI or bring your own algorithm to the tool to improve the data enrichment process by auto labeling data.

Other tools offer prediction models that suggest annotations so workers can validate them. Some features leverage embedded neural networks that can learn from every annotation made. All of these features can save time and resources for machine learning teams and will have a profound effect on data annotation workflows.

 

Outstanding benefits of automated data labeling

When working with organizations using tools to annotate images for machine learning, we find two optimal ways to apply auto labeling in data annotation workflow:

• Pre-annotate some or all of your dataset. Workers come behind the automation to review, correct, and complete the annotations. Automation cannot annotate everything; there will be exceptions and edge cases. It’s also far from perfect, so you must plan for people to make reviews and corrections as necessary.
• Reduce the amount of work sent to people. An auto-labeling model can assign a confidence level based on the use case, task difficulty, and other factors. It enriches the dataset with annotations, and sends annotations with lower confidence scores to a person for review or correction.

We’ve run time experiments, with one team using tools that have an automation feature versus another team that is manually annotating the same data. In some cases, we’ve seen auto labeling provide low-quality results which increase the amount of time required per annotation task. Other times, it has provided a helpful starting point and reduced task time.

 

 

In one image annotation experiment, auto labeling combined with human-powered review and improvements was 10% faster than the 100% manual labeling process. That time savings increased from 40% to 50% faster as the automation learned over time.

It also had a more than the five-pixel margin of error for vehicles and missed the objects that were farthest from the camera. As you can see in the image, an auto-labeling feature tagged a garbage bin as a person. It’s important to keep in mind that pre-annotation predictions are based on existing models and any misses in the auto labeling reflect the accuracy of those models.

Data annotation tools can include automation, also called auto labeling, such as Labelbox and Tagtog, which uses artificial intelligence to label data, and workers can confirm or correct those labels, saving time in the process.

While auto labeling is not perfect, it can provide a helpful starting point and reduce task time for data labelers.

 

 

Some tasks are ripe for pre-annotation. For example, if you use the example from our experiment, you could use pre-annotation to label images, and a team of data labelers can determine whether to resize or delete the labels, or bounding boxes.

This reduction of labeling time can be helpful for a team that needs to annotate images at pixel-level segmentation.

Our takeaway from the experiments is that applying auto labeling requires creativity. We find that our clients who use it successfully are willing to experiment, fail, and pivot their process as necessary.

As auto data labeling is one of the breakthroughs for a better outlook of the AI technology, specifically machine learning, we still have a lot to discover with this new term.

 

 

If you want to hear from our experts concerning the matter of Automated data labeling, please contact us for further details.

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Parallel to the fast-paced development of the Artificial Intelligence and Machine Learning market, the field of data annotation is moving forward with the most accelerating trends, both in terms of tools and workflow.

From AI-Powered Virtual Assistant to Autonomous Cars, data annotation has played an important role.

Some might think that data annotation is a boring, timid and time-consuming process, while others might deem it the crucial element of artificial intelligence’s success. 

In fact, data annotation, or AI data processing, was once the most-unwanted process of implementing AI in real life. However, with the ever-growing expansion AI in multiple fields of our daily lives, the needs for rich, versatile and high-quality datasets are higher than ever. 

In order for a machine to run, in this case, is the AI system, we have to pour training data in so that the “machine” could learn to adapt to whichever is coming at it.

With these trends in the data annotation and AI data processing market, it not only sets a new outlook for the whole market, it also proves the urgent needs for well-annotated datasets.

 

Predictive Annotation Tools – Auto Labeling Tool

It is pretty obvious that the more fields we can apply Artificial Intelligence and Machine Learning in, the more we need AI data processing. 

By saying AI data process, we also mean that we need both the data collection and data annotation.

The rapidly expanding needs of the AI and machine learning market have set a new goal for another focus of the data annotation process. As it is with the Testing market, the demands for auto labeling, or we can call it predictive annotation tools are coming to a peak.

 

Basically, the predictive annotation tools (auto labeling tools) are the tools that can automatically detect and label items with the foundation of the similar existing manual annotations.

With the implementation of the aforementioned tools, after some manually annotated data, the toolkit can subsequently annotate the similar datasets.
Throughout this process, the human intervention is limited to the minimum amount, hence saving a lot of time and effort to do such repetitive and boring tasks.

With just some scratches on the surface, auto labeling, or predictive annotation tools may be the pivotal change that will boost up the speed of the annotation process by 80%. But to put one auto labeling tool on the market, it takes years of developing sophisticated features, not to mention a large number of data types need to be put in the data annotation system of that tool. That is why you often see one tool for only one data type.

While the advantages of an auto labeling tool are undeniable, the cost for one commercial tool like that can be enormous.

 

Emphasis on Quality Control

It is sure that Quality Control plays a huge role in every process. However, the current situation only shows that QC is only circumstantial. 

In the future, data engagements at scale will be the main focus, requiring a higher emphasis on quality control.

With more data labeling solutions going into production, and later into the training model of AI systems, more edge cases will be considered.

 

Under this circumstance, it is a must that you build your own teams of QC to exclusively handle the quality of the annotated datasets. They will not work the way the old QC staff did. On the contrary, these specialized experts can function without detailed guidelines and focus on spotting and fixing issues with large datasets.

What about security? With the security, the QC team should follow a stringent process of maintaining security of the annotation process. This should be ensured throughout the whole project.

 

Involvement of metadata in data annotation process

From autonomous vehicles to medical imaging, in order for the AI system to run smoothly without glitches, a staggering amount of data is required for annotation.

Metadata is the data clarifying your data. With the same old annotations as the code snippets you put in at the Java class or method level that further define data about the given code without changing any of the actual coded logic, metadata is for data management.

 

All in all, metadata is created and collected for the better utility of that data.

If we can make good use of the metadata, any human errors including misplacing things, management malfunctions, etc. will be tackled. With metadata in hand, we will be able to find, use, preserve and reuse data in a more systematic manner.

• In finding data, metadata speeds up the process of finding the relevant information. Take a dataset in the form of audio for example. Without metadata and the management from it, it would be impossible to us to find the location of the data. This also applies to data types such as images and videos.
• In using the data, metadata gives us a better understanding of how the data is structured, definitions of the terms, its origin (where it was collected, etc.)
• In re-using data, metadata helps annotators navigate the data. In order to reuse data, annotators are to have careful reservation and documentation of the metadata.

The key to making all of this happen is data annotation. Adding metadata to datasets helps detect patterns and annotation helps models recognize objects.

With all the benefits of metadata in how we can manage and use the datasets, many firms now have grown interested in developing metadata for better management.

 

Workforce of SMEs

The rapidly growing number of the industries embracing AI, a subject-specific data annotation team is of urgent needs. 

For every domain such as healthcare, finance, automotive, etc. a team trained with custom curricular will be deployed on projects, hence expert annotators built over time. With this being done, more value and high-quality to the annotation process will be focused with a deeper approach, and this strategy will start with the validation of guidelines to time of data delivery.

 

Do you want to deploy these data annotation trends? Come and contact LQA for further details:

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The AI-Powered Virtual Assistant Market Size is estimated to be at $3.442 Billion in 2019, and this number is expected to surpass $45.1 Billion by 2027, raising by 37.7% (according to a study by CAGR). And this can all start from the simple voice annotation.

The possibility and utility of AI-Powered Virtual Assistants come from both technical and behavioral aspects. In correlation with the ever-growing demand for on-app assistance, we have the data inputs continuously poured into the AI system for data training. 

To put it another way, one of the most important features to make AI-powered virtual assistants possible is the data inputs, aka voice annotation.

 

The booming industry of AI and virtual assistant

For starters, an intelligent virtual assistant (IVA), or we can call it an AI-powered virtual assistant, is a software technology that is developed to provide responses similar to those of a human. 

With this assistant, we can ask questions, make arrangements or even demand actual human support.

 

Why are virtual assistants on the rise?

Intelligent virtual assistants are widely used, mostly for the reduced cost of customer handling. Also, with quick responses for live chat or any other form of customer engagement, IVA helps boost customer service satisfaction and save time.

Besides external performance as above, IVA also collects customer information and analyzes conversation & customer satisfaction survey responses; thereby, helping organizations improve the customer and company communication.

 

Intelligent virtual assistants can play as the avatars of the enterprises. They can dynamically read, understand and respond to queries from customers, and eventually reduce costs for manpower in different departments. 

We can see many of those IVAs in large enterprises as they can help eliminate the infrastructure setup cost. This is why the revenue for IVA are so high in recent years and perhaps in the years coming.

 

What can virtual assistants do?

The usability and adoption of AI-powered virtual assistance are everywhere. We can see it in our operating systems, mobile applications or even chatbots. With the deployment of machine learning, deep neural networks and other advancements in AI technology, the virtual assistant can easily perform some certain tasks.

 

Virtual assistants are very common in operating systems. These assistants help in setting calendar, making arrangements, setting alarms, asking questions or even writing texts. A multitasking assistant like this is on the large scale, and we might think that these applications are limited within  operating systems only.

However, with the soaring numbers of mobile users and mobile apps, many entrepreneurs and even start-ups are beginning to implement a virtual assistant just within their product apps. This leads to the rising demand for the data input required in different fields.

For example, a healthcare service app requires specific voice annotations regarding medical terms and other healthcare-related matters.

In the report of ResearchAndMarkets.com concerning Global Intelligent Virtual Assistant (IVA) Market 2019-2025: Industry Size, Share & Trends, it is indicated that:

• Smart speakers are developing with the fastest pace and emerging as the major domain for IVA
• Still, Text to speech is the largest segment in IVA. It is estimated to reach a revenue of over $15.37 Billion by 2025
• The country with the dominance in the market of IVA is North America with the main industry of healthcare.
• The key players are Apple Inc., Oracle Corporation, CSS Corporation, WellTok Inc., CodeBaby Corporation, eGain Corporation, MedRespond, Microsoft, Next IT Corporation, Nuance Communications, Inc., and True Image Interactive Inc.

Through the report, we can see that the potential to develop and grow the AI-powered virtual assistant market is on fast-paced growth. For every different domain, we have a different approach for the implementation of IVA.

For better service and business development, enterprises demand effective customer engagement, hence the growing number of virtual assistants to be implemented in different products.

Currently, the intelligent virtual assistant market is majorly driven by the BFSI industry vertical, owing to its higher adoption and increasing IT investment. However, automotive & healthcare are the most lucrative vertical segments and are likely to maintain this trend during the forecast period.

 

How can voice annotation help the IVA?

As Virtual Assistant appears in almost every aspect of life, including calling, shopping, music streaming, consulting, etc., the requirement for voice data processing continues to grow. Besides the speech to text and text to speech annotation, more advanced forms of part of speech tagging or phonetics annotation are also in high demand.

 

For a IVA system to operate properly, the developer has to consider different approaches of interaction methods, including:

• Text-to-text: Text-to-text annotation is not necessarily directly related to the operation of IVA. Nevertheless, labeled texts help the machine understand the natural language of humans. If not done properly, the annotated texts can lead a machine to exhibit grammatical errors or wrongly understand the queries from customers. 
• Speech-to-text: Speech-to-text annotation transcribes audio files into text, usually in a word processor to enable editing and search. Voice-enabled assistants like Siri, Alexa, or Google Assistant are fine examples for this.
• Text-to-speech: Text-to-speech annotation enables the machine to synthesize natural-sounding speech with a wide range of voice (male, female) and accents (Northern, Middle and Southern accent). 
• Speech-to-speech: Speech-to-speech is the most advanced and complicated form of annotation. With the data input of this, the AI can understand the speech of users, and then answer/perform accordingly.

Whichever of the above, we still have to collect data, voices, speeches, conversations, and then annotate them so that machine learning algorithms can understand the input from users.

Voice annotation service requires much effort to deliver understandable and useful datasets. It also takes much time to even recruit and train the annotators, not to mention the on-job time.

If you want to outsource voice annotation, contact LQA now for instant support.

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One of the most popular use cases of AI and Data Annotation is Autonomous Car. The idea of Autonomous Cars (or Self-Driving Cars) has always been a fascinating field for exploitation, even in entertainment or actual transportation. 

This was once just a fictional outlook, but with the evolution of information technology and the technical knowledge obtained over the years, autonomous cars are now possible.

 

Perhaps the most famous implementation of AI and Data Annotation in Autonomous Cars is Tesla Autopilot, which enables your car to steer, accelerate and brake automatically within its lane under your active supervision, assisting with the most burdensome parts of driving. 

However, Tesla Autopilot has only been confirmed of success in several Western countries. The real question here is that: “Can Tesla Autopilot be used in highly congested roads of South-East Asia countries?”

 

The role of Data Annotation in AI-Powered Autonomous Cars

Artificial Intelligence (AI) is the leading trend of Industry 4.0, there’s no denying that. Big words and the “visionary” outlook of AI in everyday life are really fascinating, but the actual implementation of this is often overlooked. 

In fact, the beginning of AI implementation started off years ago with the foundation of a virtual assistant, which we often see in fictional blockbuster movies. In these movies, the world is dominated by machines and automation. Especially, vehicles such as cars, ships and planes are well taken care of thanks to an AI-Powered Controlling System.

With the innovation of multiple aspects of AI Development, many of the above have become true, including the success in Autonomous/Self-Driving Cars.

 

Training data with high accuracy

The two important features of a self-driving car are hardware and software. For an autonomous car to function properly, it is required to sense the surrounding environment and navigate objects without human intervention.

The hardware keeps the car running on the roads. Besides, the hardware of an autonomous car also contains cameras, heat sensors or anything else that could detect the presence of objects/humans.

The software is perhaps the standing point of this, in which it has machine learning algorithms that have been trained. 

 

Labeled datasets play an important role as the data input for the aforementioned learning algorithms. Once annotated, these datasets will enrich the “learning ability” of AI software, hence improving the adaptability of the vehicles.

 

With high accuracy of the labeled datasets, the algorithm’s performance will be better. The poor-performing data annotation can lead to possible errors during a driving experience, which can be really dangerous.

 

Enhanced Experience for End-users

Who wouldn’t pay for the top-notch experience? Take Tesla as your example. Tesla models are the standard, the benchmark that people unconsciously set for other autonomous vehicle brands. From their designs to how the Autopilot handles self-driving experience, they are combined to create a sense of not only class but also safety.

How Tesla designs their cars is a different story. What really matters for the sake of their customers is safety.

Leaving everything for “the machine” might be frightening at first, but Tesla also guarantees that through many of the experiments and versions of the AI software. In fact, it was proven that Tesla Autopilot can easily run on highway roads of multiple Western countries.

 

We might have seen the footage of how Tesla Autopilot Model X was defeated on the highly congested roads of Vietnam. However, we have to take a look back at the scenario in which we need an autonomous car the most. 

The answer here is the freeway and highway. And Tesla can do very well on these roads.

The role of data annotation in this case is that through the high-quality annotated datasets, the machine is trained with high frequency, therefore securing safety for passengers.

 

The future of autonomous vehicles

We don’t simply jump from No Driving Automation to Full Driving Automation. In fact, we are barely at Level 3, which is Conditional Driving Automation.

• Level 0 (No Driving Automation): The vehicles are manually controlled. Some features are designed to “pop up” automatically whenever problems occur.
• Level 1 (Driver Assistance): The vehicles feature single automated systems for driver assistance, such as steering or accelerating (cruise control). 
• Level 2: (Partial Driving Automation): The vehicles support ADAS (steering and accelerating). Here the automation falls short of self-driving because a human sits in the driver’s seat and can take control of the car at any time. 
• Level 3 (Conditional Driving Automation): The vehicles have “environmental detection” capabilities and can make informed decisions for themselves, such as accelerating past a slow-moving vehicle. But they still require human override. The driver must remain alert and ready to take control if the system is unable to execute the task. Tesla Autopilot is qualified as Level 3.
• Level 4 (High Driving Automation): The vehicles can operate in self-driving mode within a limited area.
• Level 5 (Full Driving Automation): The vehicles do not require human attention. There’s no steering wheel or acceleration/braking pedal. We are far from Level 5.

With Tesla Autopilot qualified as Level 3, we are only halfway through the journey to the full driving automation.

However, we personally think that the matter of these Level 3 vehicles is the training data for the AI system. The datasets that have been poured into this are very limited, possibly can be compared to just a drop in the ocean.

 

To train the AI system is no easy task, as the datasets require not only accuracy but also high quality, not to mention the enormous amount of them.

The speed in which Tesla or any other autonomous vehicle company is going for is quite high in order to be ahead of the competition. Instead of doing it themselves, these companies often seek help at some outsourcing vendor for better management and execution of data processing. These vendors can help with both data collecting and data annotating.

Want to join the autonomous market without worrying about data annotation? Get consults from LQA to come up with the best-fitted data annotation tool for your business. Contact us now for full support from experts.

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

 

With the widespread adoption of Automation Testing, businesses are now spoilt for choices of Automation Testing Tools. 

A plethora of automation tools, both open-source and vendor-source – have granted the market the opportunity to explore and deploy the best-suited ones for their firms. 

You must have gotten acquainted with the theory of how to choose the right automation, but turning this into practice is another story. As listed below are the top 10 trusted and curated automation testing tools with high credibility, divided into 3 categories of web app, mobile app and desktop app.

 

What testers need to know about testing: Principles, Skills & Testing phase

Enhance your software testing capabilities with global quality standards

 

Automation Testing Tools for Web App

Web App testing takes up a high percentage of the Quality Assurance market, hence the diverse and varied automation testing tools. These tools, in this case, should be feature-rich and support cross-browser compatibility.

 

1. Selenium

Selenium is among the most popular and renowned automation testing tools as the flagship automating web application for testing purposes.

 

“It is considered the industry standard for user interface automation testing of Web applications. Specifically, 54% percent of respondents used Selenium as their automation testing tool, according to the Test Automation Landscape in 2020 Report.” – Brian Anderson (Automation advocate & Selenium committer)

 

With the assistance of Selenium, the automation testing process can be implemented across different operating systems and different browsers, thanks to its two main projects.

With the 3 main projects of Selenium WebDriver, Selenium IDE and Selenium Grid, this tool empowers testers to automate browsers in a robust and versatile manner.

With the utility of diverse programming languages of Java, C#, Python, Ruby, PHP, Perl and JavaScript, etc., Selenium supports browser-based regression testing scripts and suites across multiple environments.

The extension for Chromium browsers and Firefox add-on also assists testers in writing quick bug reproduction scripts in automation-aided exploratory testing. This exploits the record-and-playback principle for automation testing implementation.

 

2. TestComplete

TestComplete from SmartBear offers a platform of multiple modules including Web app, mobile app and desktop app. Among these three modules, the outstanding one is the web application with the utility of reusable tests for all web applications. 

With the record & playback test approach, testers can use modern programming languages like JavaScript, Python and VBScript, etc.

TestComplete majors in GUI test automation tool with the assistance of AI and ML, empowering object recognition engine and script/scriptless flexibility. CI/CD is implemented for the increase in speed of delivery.

The TestComplete tool gives you access to 2050+ on-cloud test environments, making it easier for real device maintenance, virtual machines and in-house testing labs.

Accompanied by these on-cloud testing environments is the utility of manual testing which can be applied in any phase for better test coverage and accuracy.

 

3. Katalon

Katalon is another automated UI testing tool with convenient access to any type of testers, including the ones with no programming knowledge and background.

As the pioneering name in the field of codeless automation testing tools, Katalon thrives as a productive IDE for the all-platform automated test generation, regardless of the application complexity. 

Katalon scratches the surface of codeless test scripts and digs deep into the infinite testing extension, meaning that testers of all levels and competency can work with Katalon, from newbies to experts.

Other outstanding features of Katalon are:

• Utility of automation testing in API and mobiles testing
• Ability to generate test scripts, create test cases, report results and record actions
• Support for Groovy/Java scripting languages
• Support for image-based testing
• Built-in object repository, object re-identification and Xpath

 

 

 

Automation Testing Tools for Mobile App

The 4.0 Industry development entails the upsurge of smart device users with no less than 3.8 billion smartphones being circulated in the market.

More than one-third of these devices are tablets, with 244.2 billion apps downloaded in 2020. This number is predicted to surpass 258 billion app downloads globally in 2022. The skyrocketing market of mobile apps leads to the higher-than-ever demand for the quality and faster time to market (for both new releases and feature updates)

To become the flagship of the field, one must be ahead of the curve and come to the forefront with tactics to consummate the mobile apps. The best solution for this is through the automated testing tools as follows.

 

1. Appium

 

“At this time, my team is only using Appium, but this will most likely change as other teams have automation needs with mobile devices. The main reason why Appium is a good choice for us is that it allows for tests to interact easily with both Android and iOS devices. Instead of using one toolset for Android and another for iOS, Appium combines how automation functions with each platform and puts it all into one library.” – Randall Kelley (Senior Software Development Test Engineer)

 

Trusted by many developers and testers around the world, Appium helps you automate your mobile app with well-known dev tools and many programming languages. More importantly, Appium supports API from Selenium WebDriver for in-depth development.

Appium inherited many of its features from Selenium, hence supporting cross-platform testing (in iOS and Android), a wide range of languages and multiple test frameworks.

The only hiccup with Appium is the lack of official support. Instead, it has a dynamic and supportive community for real-life problems.

 

2. SoapUI

SoapUI is one Functional Testing tool for SOAP and REST testing, supported by SmartBear. This automation testing tool does not require high knowledge of programming languages. With SoapUI, the creation of test suites, test cases and their maintenance is quick and easy.

 

SoapUI supports many features, namely:

 

Automation Testing with SoapUI

SoapUI allows automation testing with the customization of test execution to override test parameters. 

Testers have full control over the functional/load tests with the automation features of SoapUI by using the Command-Line tools bundled with the tool.

 

Technology Support from professionals

SoapUI provides support for common protocols and standards.

 

Real Services

Mimic your Real Web Services without having to wait for them to be ready or accessible. Best of all, you don’t have to build expensive full-scale replicas of your production systems.

 

Security Testing

Using a complement of tests and scans, protect your services on websites against the most common security vulnerabilities.

 

Performance Validation

Use built-in assertions to check your web service performance and to ensure that it matches user expectations.

 

Ecosystem

A big part of what makes SoapUI great is the universe of the open source community and partners around it, who have accelerated the pace of innovation on SoapUI. Another reason SoapUI is so great is that it allows anyone to develop their own set of SoapUI features as SoapUI Plugins. And SoapUI Pro adds award-winning support from the SmartBear team.

 

3. Eggplant

Eggplant is an automation testing tool specializing in Graphic User Interface (GUI). This tool offers easy operating system integration with systematic test case management. 

Eggplant utilizes a two-system model which consists of a controller machine and a system under test. 

 

Besides the two dominant operating systems of Android and iOS, Eggplant also supports other standard platforms including Windows Phone, Symbian and Blackberry.

Note: Eggplant allows testers to write and execute test scripts in a manner similar to that of actual user interaction.

One major obstacle that one may encounter during their time with Eggplant is the lack of experts. Eggplant offers a great deal of outstanding features, but its popularity is still limited in a small community, making it difficult to find experienced and knowledgeable testers to work with the tool.

 

4. Ranorex

Ranorex is another vendor-source/commercial automation testing tool that has gained a universal reputation for its powerful features in creating, maintaining and executing robust test automation projects.

With Ranorex, every action, test execution, test report, etc. is under the operational control of the Web and mobile test command center. In this center, endpoint and environments of the testing system are centrally created and configured. 

In the command center, test suites operate with flexibility. To be more specific, test cases can be combined with existing code or recording modules.

Ranorex allows custom run configurations, data binding or parameterization.

Ranorex can support both newbie testers or experts in the field. Because Ranorex is a commercial, it offers dynamic ID support and expertise support from professionals. 

 

5. Kobiton

Kobiton is featured with the highlight of a mobile testing platform. The services here focus on device lab development, and more importantly, automation testing on different platforms with real devices. 

With the absence of emulators, Kobiton offers seamless access to Real-device Testing with Scriptless Automation. 

 

“Before Kobiton we were constantly buying new phones and only the folks in our office could use them. Now, my developer and quality control person can both use the platform to test our apps before we send them to the” – Katie Bruno (WDD)

The key player among Kobiton’s features is the continuous integration of testing into your DevOps CI/CD processes. Thanks to this, regression tests are executed in a faster course, with detailed monitor of the performance metrics. Time to market of the application/releases is quicker.

Why you should try Kobiton:

• Kobiton applies the Appium open-source framework, making it possible to integrate with other Frameworks as well. These can be Appium, XCUI, Espresso, etc.
• Codeless mobile testing with easily automated test scripts. From one manual test session, you can execute hundreds of devices, all at once.
• Kobiton developed the Session Explorer platform where you can resolve your test failures with accuracy and efficiency. No more wasting your time with log files and videos.

 

Automation Testing Tools for Desktop App

1. Winium

Yet another automation testing tool utilizing the Selenium library. Winium is an open-source Automation Framework for Desktop App. This tool helps engineer testing to interact with the Windows applications easily, especially for those who have worked with the Selenium WebDriver before.

Programming languages compatible with Winium include:

• Python
• Java
• C#
• Ruby
• PHP
• JavaScript

 

Although Winium supports both Desktop Apps and Mobile Apps, it is preferred for Windows Desktop App.

The stand-out feature of Winium is the price. It is the only Selenium-based tool that allows Client-Based Application Automation. It only supports Windows. Mac and Ubuntu-based are unattended in Winium.

 

2. WinAppDriver

As in the definition issued by Microsoft, WinAppDriver is: “A test framework developed by Microsoft as an open-source project.”

Since the WinAppDriver framework is the implementation of Appium, which is a Mobile App Framework based on Selenium, it is a Selenium-like automation framework.

Since its launch, WinAppDriver has encapsulated the best-combining features of 2 frameworks, making it a flexible, easy and familiar framework to work with.

The WebDriver protocol, which is the most important standard of web & mobile app testing, allows WinAppDriver to drive any Windows apps in multiple languages, namely C#, Java, Python, Ruby, etc.

Test runners available within WinAppDriver are MSTest, JUnit, NUnit, etc.

For every automation testing approach, we have different automation testing tools, which require us to go through a thorough analysis to come up with the most suitable.

 

If you want to hear from our professional staff about what automation testing tool to implement at your business, don’t hesitate to Contact us for detailed consultation. 

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

 

Automation Testing moves at the speed of light, as the technological trend and applications are influenced by the ever-evolving change of the information technology market.

From the starting point of writing and executing the test scripts with bare support from technical tools, automation testing team now leans towards the utility of automation testing tools with various choices in terms of pricing plans, programming skills and other technical aspects.

After years of development and enhancement, the current pool of automation testing tools is now considered the pathway to success in the journey of transforming from manual to automation.

 

3 Types of Automation Tools

Automation tools are the destination for businesses and firms to come to for the optimal solution.

You must have been familiar with How to do automation testing. But do you really know the nature of them, such as their categories, price range, etc.? With this pace of development within the market, automation tools can be divided into 3 categories as follow:

 

Open-Source Tools

Open-source tools are the most common type for many firms and businesses to implement their automation testing process.

With these tools, the source code for testing execution has deliberate access. Plus, the source code can be utilized in both ways, either with full adoption or partly modification, depending on your needs.

For general utility, open-source tools allow quick access and viability for customization of an advanced test case. Hence, the use of an open-source tool requires SDETs (Software Development Engineering Testers) to have a programming background to deliberately customize the source code according to the specification of the project.

Note: Open-source tools for automation are free. In contrast, the cost of training and hiring adequate automation testing engineers is much higher than that of manual testing.

 

Commercial Automation Tools

Commercial automation tools, or vendor-source tools – are one other option for automation tools. With purchase through a monthly or annual subscription, you get access to premium features of these tools

One fine example of these features is the customer-centric service. If you encounter any problem during your time with the tool, full support 24/7 with a dedicated team will be at your service.

 

Custom Frameworks

No doubt open-source and vendor-source automation tools are the most popular ones, with a percentage of the utility of more than 80% of the market. Nevertheless, they are not always the answer, especially for niche projects. 

 

The differences and incompatibility in the testing processes, testing environments, test methodology require the testing team for a custom framework to be developed in accordance with their project’s features.

 

As compared with the open and vendor-source tools, custom frameworks are far more complicated and burdensome to develop. For most of the time, custom frameworks are the last resort for the testing team and technical experts only if the other types of tools are incompatible with the project.

 

How to Choose the Best-Fit Automation Tool?

Despite being spoilt for ubiquitous choices of automation tools, businesses can follow these 5 following steps to come up with the strategic tools best fit for your firm.

 

1. Acquire a profound understanding of project requirement

A thorough understanding of the project requirements acts as the foundation for any further development of that project, including the automation testing process.

With the undeniable improvement and enhancement of automation testing, the scope and scale of the tests and projects require minute details to alleviate the test results and proficiency throughout the operation of the testing team.

The checklist of what to be clarified includes:

•       Kinds of application (web app, mobile app or desktop app)
•       Scope of the project (number of test cases, test scripts, etc.)
•       Skill competence of the testing team

Having sorted out the checklists of project specification, businesses can indicate a strategic business analysis to put into an official project requirement. Only with the clarification of every single detail can the chosen tool integrate seamlessly with the project/application.

 

2. Devise budget and pricing plans

For any business, the ultimate target is to gain as much revenue as possible. In other words, a positive ROI for your business, in the long run, is the utmost important goal. 

Fascinating as it is, not every firm is willingly ready to invest such a big amount of capital with no ROI in the near future.

 

To successfully pull this off, the first thing you need to do is devise a budget and plan for your capital. Depending on the budget, it will be easier for you to pick the appropriate software and the suitable automation tool.

 

With the detailed plan of the expenses and infrastructure for an automation testing lab, product owner or higher-level executive can have a grasp of how the whole testing process will operate with the given revenue. Hence, the decision on what tool to be implemented will be more cost-effective and proficient. 

 

3. Determine the available skills of SDET and the programming languages

The inroads of automation testing take huge effort and knowledge of programming languages and other technical skills.

Most of the automation testing tools necessitate popular programming languages, including Java, Python, etc. The application of these programming languages is a must, but to proficiently and competently implement them in the automation testing environment is another story.

For usable and sustainable test scripts, automation testers are obligated to write code that conforms to the designated quality standards of the project. Along with this, it is also quite important to deliver test scripts that are both efficient and comprehensible, even for newbie testers. 

This could help in automation training and maintenance for later.

 

4. Clarify supported platform cross-browser compatibility

Supported platforms of the project are to be clarified as for each different platform, there are different tools to optimize the testing process.

For example, Selenium is an open-source framework to perform web testing; Kobiton specializes in Mobile and IoT Continuous Testing; Ranorex is for desktop applications.

Regarding cross-browser compatibility, testers have to consider these features:

• Different browser-OS combinations, devices, and assistive tools
• Shortlist of the most important browser-device combinations. This can be concluded from the target market, business indicators and other additional values. 
• The behavioral analysis of your target customers to come up with the most common combinations of browsers and devices.

 

5. Clarify other technical aspects

Distinct from manual testing, automation testing process demands for high skills in the use of programming languages, not to mention other aspects in the course of technology. 

With automation testing tools, the project leaders have to dig through the functions and performance of those available to come up with the most suitable lists of tools for your project. 

The services provided within the market of automation testing tools have diverse choices for clients. You can experience one’s core features in trial and then decide whether it’s worth your investment.

Check out: Top 10 Trusted Automation Testing Tools for Your Business

The technical aspects of any automation tools include:

• Script maintenance and reusability: As a key factor on how the cost of one automation testing project can escalate, testing scripts maintenance requires vast consideration for any possibilities of utility in the future. 

By putting a direct effect on the reusability of the test scripts, the automation testing team can save a lot of time when facing similar test cases.

A significant factor that escalates the total cost for test automation is script maintenance. Pre-written scripts in automation testing are fragile by nature. The ideal automation tool should come with capabilities to reduce such effort, such as eliminating object locator flakiness. On the other hand, script reusability saves you and the team a great deal of time for similar test cases as you can reuse test scripts.

• Technical support from tool provider: Professional technical support is offered within commercial tools. Normally, you can get help from tool providers via channels like direct chat or email. As in open-source tools, the support often comes from an active community with thousands of users.
• CI/CD integration capabilities: Tools with capability to integrate into the CI/CD pipelines ensure the testing continuity, making the whole process robust, dynamic and comprehensive. 
• Report record and format: With automation testing tools, the record of test results and further documentation is secured in digital format, which is stored for future reference and training.
• Keyword & Data-Driven Testing: The robust utility of Keyword & Data-Driven Testing assists test team in the extension and expansion of the test scope. In the long run, this is a strategic feature for higher ROI.
• The applied application under test: The application under test should have a schedule of releases for the most proficient preparation. What features to be updated and new points should be noted to clarify the procedure of automation testing.

 

No matter what your scale and what aspects to be tested in your product, you have to follow through the 5 steps above to catch the essence and the core of your automation testing process. You can either figure this out with your BA and developer team or you can come to experts in the field for a thorough analysis of what tool to be implemented.

 

If you want to hear from our professional staff of the Testing Industry, don’t hesitate to Contact us for detailed consultation. 

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

 

As Automation Testing is currently the emerging trend and one of the tactics that corporations use to lower the cost barrier and secure minimum leakage percentile in quality assurance, the universal questions lie upon “Why” and “How” to “From Manual to Automation Testing”.

Not as easy and simple as it appears to be, Automation Testing requires the whole transformation rather than just the transition itself. In this article, the prospects of Automation Testing and how to actualize it will be discussed.

Manual Testing is often the destination for quality control and quality assurance officers when it comes to software testing. As opposed to this, Automation Testing has been soaring in popularity with high-yielding prospects, despite many of its roadblocks and challenges.

 

Manual Testing Limitations

Manual Testing has been the most popular method of the quality assurance process in general, yet it exposes some limitations that cause many businesses to become testing-ineffective.

 

Tedious, timid and time-consuming executions

 

Manual software testing is carried out by a person sitting in front of a computer carefully going through application screens, aiming at various usage and input combinations, assessing outcomes of the expected action, and logging these observations.

 

Tests are redone often during the time of development cycles for source code adjustments, or other conditions such as changing operating environments or hardware configurations.

As in a software development life cycle, the quality assurance process has always played a vital role as this secures the well-rounded product release; hence the initial outcome of the project itself.

During software development, every developer analyzes their creations and strives for error-free scenarios. 

 

However, the reality has proven the other way round with high risks of error, and more importantly, the tedious, timid and time-consuming executions throughout the Manual Testing process.

As testers try to figure them out before the product release, they sometimes reappear no matter what they do to plan the test suites. As a result, the test executions have to be carried out in a regression manner to effectively test the software.

 

Resource-intensive process

The Manual Testing process is no easy task. It has not only unprecedented change but also a heavy emphasis on the quantity of many aspects, including:

• The data input
• The device being tested
• The popular operating system

The whole resource intensive-process of Manual Testing poses great challenges for businesses as this requires critical infrastructure with a large scope, resulting in a budget deficit.  

 

Prone to error

 

To err is human, not to mention the complexity and diversity of the testing process are always at a high level.

 

The main reason why Manual Testing is so error-prone is that the test cases might be too tedious and repetitive, causing the disinterest and focus of the testers themselves. 

One more reason for this is the misunderstanding and misinterpretation of the details of the test plans. As a result, the “cogs” in the machine now fell out of place, making the whole process dysfunctional.

With all of the factors above, the error-prone characteristics will eventually and ultimately affect the firm’s financial and reputation status.

 

Not systematic and synchronized documentation

In Manual Testing, synchronized documentation for further utility and education is not available. 

More often than not, test execution results are stored in Excel or Word files. Access to these files is restricted and not always available. The testing engineers may have difficulties in the working process, slowing down the whole operation and execution of the test cases.

 

Automation Testing Predominance

 

Software testing is the ever-evolving field of the market. It takes you to be ahead of the curve to capture the essence of the latest trends and eventually resonate with the core of your products through quality assurance.

 

Under this circumstance, Automation Testing emerges at the forefront of the software testing market, which can create and generate valuable assets for a firm. 

The 8 promising prospects of Automation Testing include:

• High yield of ROI
• Consistent regression testing
• Broad test coverage
• Accuracy and Reliability
• Faster pace
• Developers and Testers unburdened
• Reduce Human Intervention
• Records of measure quality metrics

The predominance of Automation Testing and the raise of open-source automation testing tools such as Selenium, Katalon, SoapUI, etc. encourages businesses to transition from Manual to Automation Testing.

Related articles:

How to do automation testing?

High yield of ROI

Perhaps the most important and impressive of Automation Testing is the high yield of Return on Investment.

To many business owners, the huge initial investment is an obstacle for them to implement Automation Testing. Nevertheless, it is undeniable that the investment in Automation Testing is both cost-effective and time-saving in the long run.

Testers often examine the software when changes happen in order to monitor technical quality. Whenever there is an update in the code, the software tests should be repeated. It may be analyzed on all operating systems and hardware configurations before each release of the software, which is costly and time-consuming.

 

When created, automated tests can be function and run over and over again at no additional fee. Moreover, these are much faster than normal tests. Automated software test applications can shorten the time to run repetitive checks from days to hours. That means time and resources saved are converted directly into cost savings.

 

Consistent regression testing

Regression testing is the act of running old tests to ensure that the updated software hasn’t introduced or re-introduced bugs.

The process is vital as it ensures that the validated features continue to function properly.

Over time, the test suites built in this process will grow and the amount of repetitive work builds up.

Being able to automate this can save time and reduce the amount of human work dramatically. Decreasing the amount of manual work means decreasing human error, increasing consistency; especially when it comes to large sets of tedious repetitive work.

This also means that not only do automation tests reduce the cost of running tests, but also ensure the quality of the testing process.

 

Broad test coverage

Letting machines do the work also means extending the scope and depth of tests to ensure software quality.

 

Automation tools can execute thousands of different complex test cases, providing coverage that is previously impossible with manual tests. They can look inside an application and check memory contents, file contents, data tables and internal program states to determine if the product is behaving as expected.

Even the largest QA departments cannot execute a controlled web application testing with thousands of users. However, automation tools can help simulate these virtual users interacting with the network.

This means the testers can cover more possibilities, have a better understanding of how the systems work, and later improve their performance.

 

Accuracy and Reliability

In Automation Testing, the test runs in precision according to the predefined test scripts, thus avoiding many human-related errors such as incorrect data entry.

More importantly, Automation Testing supports the programming of more sophisticated scripts to generate accurate test reports, which Manual Testing is incapable of. 

Thanks to the implementation of Automation Testing, the developers and testers are unburdened with timid, repetitive and tedious jobs, making it easier for them to focus on other aspects.

 

Faster pace

It appears that in every firm, the competition between firms not only revolves around the breakthrough of the product itself, it also depends on the time to market. 

With Automation Testing in hand, the outlook for a faster pace to put the product on the market is much more optimistic. Simultaneously, the constant pressure to release new features is also toned down as the regression testing can significantly speed up, thanks to the Automation Testing.

 

Developers and Testers unburdened

As in Manual Testing, the process and procedure always require many phases of tests. Accompanied with this is the work of the whole tester team, who constantly have to work under great pressure to release new products/features. Their work is timid, tedious and quite time-consuming. Sometimes, the scope of work overloads the capacity of one, exceeding the time allowance for the testing process.

With Automation Testing comes in handy, trivial and timid work like regression testing or performance testing can be executed with less headcount of manual testers, hence no more prolonged testing time.

 

Reduce Human Intervention

As Automation Testing has become a tactic for firms who want to step into the game of information technology, the limitation in human intervention poses vast opportunities for speed and accuracy through the enhancement in the workload of the IT team.

 

With repetitive, menial day-to-day tasks, the testing process is at high risk of mistakes and bugs due to overworked and tired testers.

 

With the help of Automation Testing, the IT team will no longer have to endure the trivial tests and be bogged down by the repetitive tasks. Instead, they can focus on other tasks which require a higher level of knowledge and skills, hence eventually improving overall performance and productivity.

 

Records of measure quality metrics

The measure quality metrics in Automation Testing requires test writers to carefully consider the unique aspects of the environment and the application of the products. 

With Automation Testing, metrics of the performances and functionality of the products are well-chosen and then stored in confidentiality for the later steps of the testing process.

Automation Testing is the on-trend way to go with the whole Quality Assurance process. Despite the misconception of the complete elimination of human touch in automation testing, manual testing is still of paramount importance for test cases and test scripts. 

Perhaps the execution of Automation Testing is a cross between strategic manual testing and high-technological automation testing. It is a tough trick to pull off, but once you successfully employ Automation Testing, the outlook for better ROI and vast opportunities is broadened. It is just a matter of time for your firm to resort to automation testing for higher revenue.

 

Ready for your journey in digital transformation? Contact us now for the optimal solutions of Automation Testing. Just a few touches and your firm is fully prepared for the transformation from why Manual Testing to Automation Testing with the support of LQA.

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