5 key AI Testing Solutions To Improve Your Product’s End-User Experience

Introduction

“Customer Experience is the new battlefield” –  Chris Pemberton, Gartner

Product development involves careful planning, innovation, automation, time, and resources. One really vital piece of the development process is product testing for quality, reliability, and security. However, gone are the days when merely testing software against the business and technical requirements sufficed. Your application or software may work as defined, but Are your customers happy and satisfied? Does your product meet the user’s needs?

One thing that today’s customers won’t appreciate is bad Customer experience (CX). Customer satisfaction can make or break your business. Instant gratification is what today’s customers look for. Thus, CX is the biggest factor driving customer loyalty. Most companies including the FAANG have already made great strides into developing next-gen apps and are integrating AI across their tech stack for delivering higher customer experience. 

Delight the customer and beat the competitor – is what all organizations are focused on. And this is not just online retail, it’s across every industry like finance, healthcare, media.

So, how do you measure Customer Experience?

Gartner in its recent study emphasizes the importance of the 5 types of metrics such as customer satisfaction (CSAT), customer loyalty/retention/churn, advocacy/reputation/brand, Quality/operations, and employee engagement to measure customer experience.

These metrics are a measure of how well your products and services meet your end users’  expectations. 

But how can executives guide their organizations to reap the benefits from AI and exploit the benefits that beckon?

One way is to put the customer front and center during software development and testing. Artificial intelligence-enabled customer journey analytics can find answers to important CX queries like:

• How can we serve the customer better to have an improved CSAT score?
• How can we detect defects before the Customers?
• Which features should you prioritize to improve CX and achieve business results?

Where does AI Testing come into the picture and how will it benefit the customer?

As per World Quality Report, demands for quality-at-speed and shift-left have placed the onus of ensuring end-user satisfaction on quality assurance teams.

To advance the digital transformation and enhance customer experiences, QA must break loose from their traditional bug testing shackles and embrace frictionless, AI-powered automation, and continuous delivery and continuous testing approach. Providing a higher quality, more reliable product to market faster, or getting answers to queries faster (or immediately) provides a richer, more positive experience for the customer, and thereby a competitive advantage for the company.

Taking the customer experience to the next level

Here are 5 key AI drivers to catalyze high-quality, agile software delivery that can add value to business applications, through its varied technologies and will revolutionize the customer experience.

Drivers for good CX	Issues with Traditional Testing	How AI Testing helps improve CX
Good product quality	Limited time for end-to-end testing. This translates to a False sense of Quality – leading to customer dissatisfaction.	AI-driven exploratory testing
Defect-free software	New feature releases outpace the regression test cycles leading to increased defects	Enhanced Defect Tracing using Defect Predictor
Adapt to changing requirements	In this ever-evolving environment, requirements keep changing, and adapting quickly to the changes is a hassle	Self-Healing
Address the risks/Trust with data sensitivity	External security threats make all systems vulnerable. Hence, protecting sensitive information becomes crucial to business growth and ensuring customer trust.	Equal focus on Performance, Load and Security Testing

These solutions are enabled by Webomates patented tools that work as accelerators for faster and better delivery. Webomates uses advanced intelligent test automation framework  to continuously test your build across platforms and devices.

1. Optimize the customer experience by AI-driven exploratory testing
   Not all defects are due to a  developer’s coding errors. What happens when there are zero defects in the code but it still fails? Go exploratory – A place where no scripted test has gone before!
   The basic aim of exploratory testing is to pinpoint how a feature works under various conditions and that could be against different devices, browsers or operating systems as well. It helps you scrutinize the potential risks by combining exploratory testing with regression testing.
   
2. Find your defects before the user does!
   With AI-based automated testing, one can increase the overall depth and scope of tests resulting in software quality improvement. The speed at which AI (Intelligent test analytics) can reveal insights allows development and testing teams to resolve defects faster than ever before. 
   Working together shifts the gear from last-minute testing to early defect identification and resolution, thereby rapidly speeding up the time-to-market for every release!
   
   
3. Power of Self Healing Test Automation Framework
   When new changes are introduced, there is a chance that the automation may fail due to the predefined test scripts. It is then very difficult to identify which test cases should be modified or added. 
   Webomates applies AI and ML algorithms to its self-healing test automation framework to dynamically adapt its testing scope to the changes. With such nimble-witted features, the test scripts adapt thus, making testing a lot easier.
   
   
4. Create exceptional digital experience by Real-time analytics and Insights Delivery
   AI thrives on information—the more the better. For AI to impact the customer experience (CX), actionable insights must be shared in real-time. Data unification to create a single view is a must for any type of defect analytics. 
   For every incremental build, Webomates CQ can create, execute, maintain, analyze test cases and generate defects for browsers, mobile, Windows, and API applications. The exact state of the system in terms of defects is known after every check-in.
   With its power to gather and analyze data in real-time, AI is helping in getting a better understanding of the defects and their patterns, and eventually creating an enhanced customer experience strategy.
   
   
5. Deliver business value by addressing the risks that emerge across the digital ecosystem – Conduct in-depth performance, Security Testing
   Imagine your application runs perfectly fine with zero defects, and your customer is happy too. But then one security incident occurs and all your efforts go in vain! Breach of trust is the biggest no-no in the CX journey.
   Webomates has proven records of exhibiting agility in scaling up its testing services based on the changing requirements. The use of the  Shift-left testing approach helps in building a good product where UI, API, Load, and Security are not left out to be tested as a different component at the end. 
   Webomates CQ platform helps to achieve effortless software testing and keeping customers happy knowing that their sensitive client data is secure.
   

Conclusion

It is evident that AI has already begun to create a tangible impact.

Webomates CQ  platform is designed to reduce test cycle duration and mission-critical defects by more than 50% by applying Machine Learning and Artificial Intelligence to software testing. The testing is optimized by combining the patented AI testing platform using multiple channels of execution like Automation and AI with crowdsourcing and manual testing.

This helps in filling the gap with the ability to connect the dots between customer behavior and business outcomes, enabling cross-functional teams to collaborate on the software development journey and better manage, measure, and optimize the development and testing.

The advantages of Intelligent Test Automation are limitless. All in all, AI testing is necessary to speed up the development and deployment of software releases. It not only helps organizations to save time but also helps in delivering high-quality products.

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo, or reach out to us at info@webomates.com.

If you liked this blog, then please like/follow us Webomates or Aseem.

Challenges in AI Testing

The term artificial intelligence was coined in the year 1956 at a conference at Dartmouth College, New Hampshire. Since then AI has seen certain ebbs, aka AI winters. It has been around for decades, slowly evolving.

The recent surge in the use of AI can be attributed to improved machine learning algorithms because of deep learning and the availability of massive data to learn from.

The Gartner Hype Cycle for Artificial Intelligence shares deeper insights on how artificial intelligence is making waves and impacting businesses worldwide. It also talks about how AI has found a niche place in top emerging technological trends.

In separate surveys conducted by McKinsey and PwC in 2020, artificial intelligence adoption can potentially contribute approximately $14-$15 trillion to the global economy by 2030.

Software testing has embraced artificial intelligence in its operations to address issues associated with manual testing. The rise of agile methodology and DevOps had initiated the need for shorter development cycles with faster time to market. Quality, of course, has to be the top priority. While many organizations have been working on AI automation for a while now, some are still sitting on the fence regarding its usage in their operations. This stems from the fact that certain challenges impede the adoption of AI-driven automation. Let us see what they are in the entailing section.

Business issues

• Immaturity in adopting AI/ML: Integrating and leveraging the power of AI in QA operations has to be meticulously planned and should be a well-thought process. It is important to understand why and how AI will help in making the QA processes more efficient and cost-effective in the long run.   
• Lack of long-term strategy: AI strategies have to be defined keeping in mind long-term benefits. Expecting returns from the onset leads to disillusionment. The right approach is to involve all the relevant stakeholders- business, operational and technical, to define the strategy and invest initial time in selecting the right QA process and operation to automate. Aligning the automation goals with the quality assurance goals and defining measurable KPIs enables the stakeholders to define a well-rounded AI strategy.
• Infrastructure issues: AI/ML solutions require high-end processors to cater to their need for high computational speed. With the exponential increase in the volume of data, the hardware and software scaling needs also change. The infrastructure requirements and costs associated with it are a big deterrent in AI adoption, especially for small and budget-conscious organizations. Cloud migration is a solution to this dilemma. Organizations can take advantage of a cloud computing environment with the power of parallel multiple processors without changing much in the in-house setup.
• Integration issues with the current setup: AI-based applications require higher capacity hardware, making legacy systems redundant or incompatible with new versions. Besides this, introducing AI may require modifying the current software or in some cases, a complete overhauling of the system may be needed. This has a cost, time, and resources factors associated with it.

Trust issues

• ROI concerns: There is a lot of uncertainty involving ROI on AI automation and its subsequent benefits, especially since the initial investment is high. The right way forward is to collaborate with all the stakeholders, set realistic goals, and work towards them.
• Security concerns: Visionaries like Elon Musk and Stephen Hawking have pointed that there is still a lot of grey areas around AI. AI is still evolving and with machine learning, there is a continuous and incremental evolution that introduces a factor of “unknown”. Also, the sheer amount of data at the disposal of AI systems is vesting a lot of power in a machine. Data security concerns and legalities rock the business world now and then.

Lack of expertise

• Not enough experts on board: AI is a niche field and requires a good amount of subject matter expertise. Hiring the right people and/or identifying and upskilling/reskilling the right people in-house is important to efficiently develop and manage AI systems. Also, AI systems learn, predict and analyze based on rules, patterns, and algorithms defined by these experts. The right set of skills is needed to aid in responsible and supervised machine learning.

Data management

• Contaminated and outdated data: AI systems learn from the data provided to them. The quality and quantity of data are important to get the desired and accurate results. Better the input for learnings, better the outcomes. These systems need reliable and trusted data sources to work on

Finding the right vendor

• One of the key factors to a successful AI adoption in QA Ops is to choose the right vendor who can provide a holistic intelligent automation tool with service level guarantees at the right price

Webomates has a competitive edge over many others with its patented intelligent automation and analytics tool, which provides value for money to its customers. Additionally, we provide service level guarantees to all our customers.

Webomates CQ is a matured intelligent automation tool that can be integrated seamlessly with a customer’s CI/CD pipeline within a matter of hours.

Our tool leverages the power of 14 AI engines and provides the following services at different levels of the software testing process.

We have a capable in-house team of experts who work with our intelligent automation tool to ensure that holistic results are generated.

Webomates CQ is an ingenious monetarily and technically suitable option with the ability to scale up or down as per the customer requirement. Partner with us and reach out at info@webomates.com to know more or click here and schedule a demo.If you liked this blog, then please like/follow us Webomates or Aseem.

Can Artificial Intelligence replace human in Software Testing?

Artificial intelligence is a computer science discipline that simulates intelligence in machines, by making them think, act and mimic human actions. There has been a significant development in the artificial intelligence industries, machines are automated to take rational actions and exhibit traits that are associated with humans. As days go by more and more algorithms are being created to mimic human intelligence and are embedded into machines. Software testing and development is a very important aspect where artificial intelligence is applied. With Digitalization improving human efficiency, so has improvements in AI shaped the way software is being tested.

2016-2017 Quality assurance report suggests that AI will help shape software testing by assisting humans in eliminating problems associated with QA and software testing challenges. However, if these needs are met in the software industries there is a possibility that human testers will become extinct. This calls for the question “Can Artificial intelligence replace humans in software testing?” Many software experts believe that artificial intelligence can only assist in software testing and cannot replace humans, because humans are still needed to think outside the box and explore inherent vulnerabilities in the software. Contrary to this, others think otherwise. But after critical thought and weighing both views, it appears that the former obviously holds more tangible points than the latter.

The Evolution in Software testing is continuous with the adoption of Agile and DevOps methodologies. And software development will also continue to evolve in the era of AI. Artificial intelligence is charged with creating software to understand input data versus output data. This is similar to software tests carried out by human software testers, where the tester types in an input and looks for an expected output. Today, testing tools have evolved. Automation tools can be used to create, organize and prioritize test cases. Efficiently managing tests and their outcomes remain essential to giving the developers the feedback they need.

Shortcomings of Humans in Software testing that can be positively transformed by Artificial intelligence

Although humans are considered a reliable source for software testing, humans still have its own shortcomings. This is a disadvantage to human software testers which reduces their efficiency and performance in software testing. These shortcomings are stated as follows:

• Time-consuming: The primary disadvantage of performing software testing by humans is that it is time-consuming. Validation of the functionalities of software might take days and weeks, and with the assistance of Artificial intelligence time wastage is reduced to minimal.
• Limited possibilities of testing for manual scenarios: Artificial intelligence creates a broad scope for testing contrary to the limited scope available to the human testing scenarios.
• Lack of automation: Manual testing requires the presence of the software tester, but testing using artificial intelligence can be done steadily without much human intervention.
• In a large organization without the help of Artificial intelligence automated tools in software testing, there will be low productivity.
• Manual testing is not always 100% accurate as it can be exposed to certain errors which may elude the software tester. Some glitches in the software are usually not recognized by the software tester, validation only occurs in certain areas and others are ignored. With AI coverage as well as accuracy can be improved.
• Scalability issues: manual testing is a linear process and happens sequential manner. this means that only one test can be created and done at the same time, trying to create more test from other functionalities simultaneously can increase complexity.
  
  

You may like to read

• Importance of Artificial Intelligence Software Testing Tool
• Black – Box Testing Explained

What are the advantages and disadvantages of Artificial intelligence testing tools in software testing?

Artificial intelligence testing tools can work side by side with the software testers in order to achieve improved quality in software testing. Modern Applications interacts with each other through a myriad of APIs which constantly grows in complexity exponentially as technology evolve. Software development life cycle is becoming more complicated by day and thus, management of delivery time is still significant. Therefore, software testers need to work smarter and not harder in this new age of software development. Artificial intelligence testing tools have helped to make software releases and updates that happens once a month to occur on weekly or daily basis. An artificial intelligence testing platform can perform tests more efficiently than human beings, and with constant updates to its algorithm, even the slightest change can be observed in the software. But as much as artificial intelligence has positive achievements in software testing industries, it still has its corresponding disadvantages. Some of these disadvantages are reasons why human contributions cannot be neglected.

Advantages:

• Improved accuracy and efficiency: Even the most experienced software testers are bound to make mistakes. Due to how monotonous software testing is, errors are inevitable. This is where artificial intelligence tools help by performing the same test steps accurately each time they are executed and at the same time provides detailed results and feedbacks. Testers are freed from monotonous manual tests giving them more time to explore the application & able to give input for improvements or usability areas.
• Increases the Overall test coverage: Artificial intelligence testing tools can help increase the scope of tests, this results in overall improvement of software quality. AI testing tools canscan through the memory, file contents, and data tables in order to determine if the software is behaving as it is expected to and at the same time can provide more triage information or even a root cause..
• Saved time + Money = Faster delivery to Market: Due to repetitions that exist in software testing every time a new product is created or modified, a human tester is needed to solve the problem associated with each test case by creating and automating tests. This helps to solve the problem of repetition, thereby saving time and money and help achieve faster delivery. By integrating AI software testing, the overall timespan can be reduced which translates directly into cost savings.
• Going beyond the limitations of manual testing: AI testing tools or bots can automatically create tens, hundreds or thousands of virtual set of users that can interact with a network, software or web-based application. This helps software testers to execute a controlled web testing with hundreds of users thereby breaking the limitation of manual testing.
• Helps both developers and testers

Disadvantages:

• Artificial intelligencein software testing use the concept of GIGO (Garbage in Garbage Out): Most people are looking at artificial intelligent to fix all their testing ills. They hope Artificial intelligence will solve all the problems that manual testing has not been able to address. The simple fact is that a problem that cannot be solved manually cannot be solved by an AI tool as well. AI tools can only solve problems that have been solved manually and has been directed for them to solve digitally. Therefore, an Artificial intelligence tool can only do what it is told to do and cannot go beyond that.
• High costs: the high cost associated with acquiring AI tools coupled with cost needed to get it updated with time to meet the latest requirements, can make it inaccessible to individual testers or smaller organizations.
• Can’t think outside the box: Automated software testers can only do what they are programmed to do. Their capability is limited and cannot go beyond whatever algorithm or programming is stored in their internal circuit.
• Unemployment: Much human software testers won’t be needed to perform software testing Jobs because most of the positions have been occupied by automation tools. Thus, limited positions will now be available for human testers to occupy.

Areas, where artificial intelligence can assist and dominate in software testing, include the following:

• Generating test case scenarios
• Generating automation script
• Predicting the defects as per code changes
• Reducing the automation false failure
• Self healing of automation script
• and many more to come….

In conclusion, assistance of artificial intelligence in software testing is significant and has helped achieve tremendous results in software testing, but more emphasis is laid on inevitable human contributions. With every passing day, as artificial intelligence finds its way into Software testing and other quality assurance fields, organizations are contemplating whether it should be adopted wholly within their quality assurance departments. But evidently, Quality assurance cannot do without human contributions. The salient benefits of AI in software testing are these: it goes beyond the limitations of manual testing, it helps achieve improved accuracy and efficiency in bug and algorithm testing, and it saves time and money involved in software development. It is evident that in the long run, AI will not only be confined to helping software testers, but will be applicable to all roles across software development to deliver top quality software to the market. So answering the subject matter question we can see that the extinction and replacement of Humans by AI is fallacious.

Webomates offers a regression testing service, CQ, that uses manual testing (test case- and exploratory-based), automation,  crowdsourcing with Artificial Intelligence to not only guarantee Full regression in under 24 Hours but also to provide traiged defects. Our customer spend only 1 hour a week on a Full Regression of their software.

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com.

10 Ways To Accelerate DevOps With AI

 Software programming has evolved over the decades and consequently, testing, which is an integral part of software development, has also undergone a series of changes.

It all started with ad-hoc testing by developers and testers, traversing through the era of manual and automation testing, to continuous testing which is integral for DevOps. The introduction of artificial intelligence in software testing opened doors for faster and reliable testing solutions which helped in delivering high-quality end products.

Read a detailed account of this journey on our blog “Evolution of software testing”. The focus of the current article is on how artificial intelligence is changing the face of software testing by accelerating DevOps

10 ways to accelerate DevOps with AI

DevOps helps organizations in keeping pace with the market dynamics by building, testing, and releasing the software faster. Multiple build-test-release cycles generate massive data which is then monitored and analyzed for improvising the next cycle. It is difficult to sift through such a huge amount of data manually after every cycle, especially if the time is of the essence.  DevOps augmented with AI is a perfect solution to improvise and speed up CI/CD/CT pipeline in a reliable manner. This is just one example where AI aids in increasing DevOps efficiency. There are more ways in which AI can help in accelerating DevOps, covered in the entailing section.

1. Codeless testing :

Codeless testing frees up the testing team from the tedious task of writing test scripts and makes way for them to focus on other important tasks. It not only increases efficiency by saving time and resources but also expands the scope of test automation.

We have covered the top benefits of codeless testing in our blog. Do take out some to read it by clicking here “Top 5 benefits of codeless testing”.

2. Better test coverage:

AI algorithms are equipped with the capabilities of diving deep into the massive volume of data at its disposal and identify multiple test scenarios that can expand the test coverage, which is a herculean task if done manually. It also uses its analytical capabilities to prioritize the tests based on predictive analysis.

3. Improved test accuracy and reliability:

Multiple release cycles in a short time involve executing a large number of test cases. While automation has taken this task from the tester’s hands, AI has gone a step further by improvising and evolving the testing with every testing cycle. This makes testing more reliable, efficient, and accurate.

4. Self-healing test automation:

Agile development leads to frequent changes in code due to defect rectifications and new/updated requirements. This consequently leads to changes in test cases. AI algorithms integrated with the test automation framework can understand these changes and effortlessly self-heal the tests and re-execute them within the same cycle, thus speeding up the entire testing process.

5. Effective defect management :

Defect management is an important aspect of software testing. Its scope is not limited to development and testing but also extends to defects encountered by the end-users when they use the system. Using artificial intelligence to troubleshoot and manage these defects significantly improves QAOps and DevOps. Read our blog “Intelligent analytics with AI testing” for further details.

6. Improved and foolproof defect tracing:

Defect identification, triaging, and tracing the defect to its origin is a key task of defect management. Defects can be introduced at any stage of development, right from conceptualization to any point till realization. Debugging and tracing is a time-consuming exercise. AI-based tools can perform this tedious task effortlessly.

7. Integrating predictive analysis with test results:

Testing result analysis enhances the software development process by identifying and aiding the teams in managing the flaws in their test strategies. AI-based testing tools constantly learn and update their knowledge base with every test cycle based on test result analysis, and apply that knowledge to improve software testing by detecting even minor changes and predicting the test outcome. It not only expedites the whole process but also makes it more intuitive and accurate.

Based on data collated from past test results and current test strategies, AI-based testing tools can mitigate risks by executing test cases for high-risk modules. This predictive analysis can optimize the testing process by a major degree. We have covered the test optimization aspect in our blog “How intelligent automation optimizes your testing”. Do spend some time reading it for better insights.

8. Enhanced test failure analysis:

Test failures are part and parcel of DevOps. It is important to manage these failures by analyzing them to understand the root cause and how it can be addressed to prevent such scenarios in the future. The AI model not only segregates the defects based on their priority but also analyzes the defect to understand the cause and notifies the teams accordingly. AI-based algorithms can understand the patterns and predict potential issues. These predictions aid the development teams to fix the issues before it is too late.

Do you want to know more about Test failure analysis with AI? If yes, then read our article on the subject by clicking here. 

9. Better collaboration and communication:

Coordination, collaboration, and communication are important cogwheels of the DevOps cycle. The test results and insights need to be shared with all the stakeholders, technical and business. It aids them in improving their business and technical strategies for accelerated development and delivery. AI helps the teams in staying updated with every aspect of testing. The test results are analyzed and reported to the stakeholders instantly. It helps in early intervention by the concerned team/person to address the issue.

10. Continuous feedback loop :

DevOps can be successful if the feedback generated at every stage of development and testing is continuously shared with all the stakeholders in an easily comprehensible format. Any delayed or skipped feedback can have an adverse effect on the software development process. AI-based tools can issue timely alerts to the teams about any problems/defects and aid them in prioritizing and managing them effectively. This comes in handy, especially when the whole CI/CD/CT pipeline is churning out huge data with every cycle which can overwhelm the testers if done manually.

Accelerating DevOps with Webomates

AI enhances the benefits of test automation and further accelerates DevOps’ performance and productivity. Using artificial intelligence engines, QA teams can trigger unattended testing cycles. It is the AI engine’s job to execute the tests, identify and report the right defects and suggest a course of action.

Webomates’ AI engine checks all the above-mentioned points and helps their customers in developing a holistic DevOps strategy by seamlessly integrating with their CI/CD pipeline.

Webomates provides AI testing solutions with intelligent analytics which aids in accelerating development cycles ensuring high-quality results. The following figure highlights the Webomates’ AI testing capabilities which act as prime accelerants for the DevOps cycle.

Our codeless engine has an AI discovery and generation tool that generates test scripts that are compatible with multiple automation frameworks. AI Modeler engine aids in generating and automating the right test cases.

Webomates’ AI codeless engine effortlessly modifies (heals) the test cases, scripts and re-executes them within the same test cycle. Healed test suites lead to faster testing and development, thus speeding up the entire release process. Our self-healing testing process is covered in detail in our blog “Self-healing- Automate the automation”.

Our ingenious AI defect predictor can identify false positives with 99% accuracy. It can predict defects much earlier in the test cycle, thus saving thousands of man-hours spent in triaging. 

AI test package analyzer provides a continuous feedback loop that ensures that requirement changes are traced at every level and updated accordingly.

Defects discovered during testing are triaged and detailed feedback is provided to the stakeholders via real-time alerts.

A comprehensive testing report and informational video are generated for all the tests. It aids the CQ users in having a holistic view of the testing results of their application. The testing and development team can access these reports at any point in time for analysis and quick resolutions. This adds significant value to the QA Ops process.

Webomates CQ is a financially and technically suitable option with the ability to scale up or down as per the customer requirement.

We can conduct testing as per the scope of the build saving thousands of man-hours.

• Full regression test which takes 24 hours
• Overnight regression at the modular level which takes 8 hours
• CI/CD that may take 15 minutes to 1 hour.

Webomates has a competitive edge over many others with its patented intelligent automation and analytics tool, which provides value for money to its customers. Additionally, we provide service level guarantees to all our customers.

At Webomates, we continuously work to evolve our platform and processes, to provide guaranteed execution, which takes testing experience to an entirely different level, thus ensuring a higher degree of customer satisfaction.

Partner with us for accelerating your DevOps. Click here to schedule a demo, or reach out to us at info@webomates.com .

If you liked this blog, then please like/follow us Webomates or Aseem.

How Intelligent Automation optimizes your Testing?

 

Intelligent Automation: Increasing the value of automation testing 

Imagine having the ability to self evolve and heal whenever there is an impediment – helping you to adapt to the change and also increase efficiency and productivity. When the key differentiator to any business in this competitive digital era is achieving end-user satisfaction, delivering “quality-at-speed” plays a crucial role in achieving it. In the last few years, there has been a conscious effort in the world of software testing to translate the manual test cases to automated testing to achieve Quality-at-Speed, lower the risk factor and improve the test coverage.

With the advent of automation, manually intensive tasks are being executed faster with reduced human errors. Test Automation is a method in software testing that leverages automation tools to control the execution of tests. Test automation is also called automated testing or automated QA testing. Ultimately, Automation tools are best suited for tasks, processes and workflows that have repeatable, predictable interactions with other IT applications.

The value of Automation in today’s world is far greater than ever. It may have started with a goal to reduce the costs for repetitive tasks. However, automating just to reduce cost is not what automation is all about. In fact, the journey gets all the more exciting with disruptive technologies like Artificial Intelligence and Machine Learning, which take automation to the next level!

Unlike Automation, which is designed to automate routine, repetitive tasks, intelligent automation goes a step further and provides the capability to automate non-routine tasks by leveraging AI and ML to redefine the ways of Software Testing and solve complex problems.

So, what exactly is Intelligent Automation?

Intelligent Automation is a layer on top of the normal automation that fixes most of the problems in automation, helps reduce human work and gain new capabilities beyond human abilities.

To gain a clear overview of the key differences, let’s compare the two terms on key parameters:

Parameters	Automation	Intelligent Automation
Technology and Focus on process	Automation is process-driven. Focuses on automating repetitive and, rule-based processes	IA is all about data-driven processes, Incorporates artificial intelligence (AI) and Machine learning (ML) technologies
Test case generation and maintenance	Test Automation cases are high maintenance and are not reusable resulting in higher maintenance costs and lower test case generation efficiency	Uses Model based testing. TDD/BDD  approach is used and the test cases are generated and maintained automatically resulting in reduced maintenance cost
Ability of self evolve	Follows rules to automate the tasks that has no variations, is restricted to repetitive tasks	Learns and adapts to data in real-time with the self-healing capability .

Now that we are clear on the key differences, let’s explore the use cases for Intelligent Automation in Testing services.

Intelligent Automation Use Cases

In “Predicts 2018: Application Development,” Gartner forecast that applying AI and machine learning to quality assurance (QA) will help identify how AI technologies can support new ways of working in DevOps, mobile and IoT environments. By 2022, 40% of application development (AD) projects will use AI-enabled test set optimizers that build, maintain, run and optimize test assets

Gartner recommends that organizations should Increase application testing agility by working with application leaders to explore IA use cases, such as test optimization, defect prediction, model-based testing, test data generation and test insights.

Intelligent Automation with Webomates- What makes Webomates stand apart!

Explore Speed, scalability and predictability with Webomates Testing-as-a-service patented AI testing platform. With this platform, we are at the forefront of Intelligent Automation.

1. Model based testing: An automated design of test cases from a model.
   
   In the traditional model of testing, testers write the test cases based on the requirements. However, at times the requirements are ambiguous or they keep changing frequently resulting in poor quality test cases. A model based testing approach addresses these challenges by automatically generating the test cases and scripts from a model.
   The definition of MBT in Wikipedia: “Model-based testing is software testing in which test cases are derived in whole or in part from a model that describes some (usually functional) aspects of the system under test (SUT).“
   
   Test as you Develop
   
   Webomates CQ Human assisted platform goes through the application to generate the baseline test cases. Using its advanced AI, it creates up to 2,000 test cases in 4 weeks that are relevant, appropriate for the existing release and will be relevant for the new release and software version. This automated way of designing test cases results in increased test coverage.
   
   Webomates advanced humans assisted AI system creates hundreds or thousands of test scenarios, runs the tests and provides the user with pass/fail reports, triages the pass/fail results and identifies and creates defects for the user of the platform to review.

2. Defect prediction
   
   Whenever an automation test suite is executed, the result is a pass or fail report, depending on whether the actual result matches the expected result or not. However, a failure can also be a False Fail which means there may be no defect and the system may be working as expected. False Fails means that it is unclear whether the test case has passed or failed. A lot of time is spent in understanding whether or not there is an actual defect in the system.
   
   AI Defect Predictor tool – Know your defect in 20 seconds!
   To avoid the massive amounts of time spent in analyzing the defect, Webomates created an AI Defect Predictor tool that shares true Pass and true Fail reports with the development team along with an in-depth analysis of automation failures to help them reduce their triage time. 
   For 300 test cases with a failure rate of 35% (105 failed test cases), it usually takes 12 hours to triage the results and identify the false positives. Using the Defect Predictor, the time taken drastically reduces to 3-4 hours.
   
   Webomates shares a comprehensive triaged defect report that includes:
   • Defect Summary
   • Steps for replicating the defect
   • Video of the actual bug instances
   • Priority suggestion
   • Test cases mapped with the defect

Webomates thus helps the teams deliver higher quality releases while saving thousands of hours removing roadblocks and drag on productivity.

3. Test optimization
   
   Test optimization refers to making the test process more time and cost-efficient without compromising the accuracy of results.
   
   Imagine you have created 5 web pages however there are changes done only to 2 of those web pages. Do you need to run all test cases? No! You just identify the test cases that are modified and run only those cases! You need to run the right tests at the right time. But that’s just not enough!
   
   At Webomates, we follow the mantra of – Right tests at the right time, in the right way!
   
   Our platform is designed to reduce test cycle duration and mission critical defects by more than 50% by applying Machine Learning and Artificial Intelligence to software testing. The testing is optimized by combining the patented AI testing platform using multiple channels of execution like Automation and AI with crowdsourcing and manual testing.

4. Test Insights
   
   Test Insights gives you the analytics from the system of patterns inside the product. It helps you find the locus of defects. Once you have insights into the defect inducing changes, you can facilitate the defect fixing process.

In a Snapshot – The Webomates’ AI Impact

14 AI Engines
5 Patents Granted 
5 Patents Pending

There are 14 AI tools that are used in the entire life cycle right from test case estimation, test case generation and test model generation to reduce the overall set up time from months to weeks and optimize application testing for quality and speed using Intelligent Automation. We help to accelerate value to customers by empowering the QA teams with such real-time self-healing capabilities using the new-age intelligent technologies and help in realizing the true business value and also empower the organizations in providing value to the customer.

• 100% execution of test cases
• 200% increase in Development Efficiency 
• 11x increase in Feature Velocity   
• 90% Reduction in Defects reported by end users

Conclusion

The power combination of Webomates AI and ML powered Testing along with Shift-left testing approach is bringing a revolutionary change in the testing era to create a more focused application development. 

To maximize the benefits, Webomates CQ believes that following a formal quality assurance process is imperative for a successful release. This gives organizations the capability to focus more on the business value and customer experience.
Our QA team embeds intelligent automation and continuous testing principles right across the software delivery pipeline. With a perfect blend of Agile, DevOps, patented AI Defect Predictor tool and a test automation framework, Webomates can help you achieve a hassle-free release, every time!