Need and Scope of AI in Software Testing

AI in Software Testing

With advances in technology, software testing is also scaling new heights. Today it is not just automation of software testing, which is making headlines; the buzzword is AI or Artificial Intelligence.

Software testing is imperative to safeguard an app or software from potential errors and performance failures. In this, the software developed is accessed and analyzed for plausible risks under a created or planned environment. The leap from manual testing to automation testing was a result of a demand for quick delivery of error-free software.

Today the delivery schedules have become shorter and the need for precision greater. Thus software testing needs to take up a smarter approach to meet these new demands, and the key lies in AI or artificial testing.

AI changing software testing dynamics

Even while the use of AI in software testing is still in its nascent stage, people are increasingly getting drawn to its implementation. With software testing becoming automated, the actual testing could soon get executed by AI so that the QA demands and testing challenges can be easily met. Software development is no longer an annual, monthly or even weekly affair.

Today software gets developed and delivered on the same day. This has given rise to a scenario wherein the execution of the test codes gets done by the AI or machine. The biggest advantage is that AI can learn and enhance its capabilities with minimal human contact. Thus the implementation of AI can be said to handhold software testing into taking the big leap wherein the software can heal itself based on the tests, diagnosis and the analysis that it does.

AI and Continuous testing

The demand for innovative software has risen drastically. Thus a need was felt for the implementation of DevOps and the Agile environment so that the accelerated delivery schedules could be easily catered.

There is also talk of the implementation of continuous testing so that testing can be conducted earlier into the software development and multiple running of the tests become hassle-free. But this will not be enough to cater to the demands of the customers in the near future. Additional support will be required so that a positive user experience can be achieved by implementing technical complexity and very high-performance speeds.

AI or artificial intelligence and ML machine learning are two terms which are used interchangeably. They work in tandem so that complicated phases involved in software development and its testing can be executed. AI thus can become the future of software testing by automating even those testing arenas where human intervention is still expected. The possibility of continuous tests can enable continuous integration and enhanced continuous delivery.

Impact of AI on software testing

Interestingly, AI has the capability for detecting even the smallest of errors. It also needs lesser maintenance and can be used to explore new paths which would ultimately help to simplify and improve efficiency and decision-making process. Thus there is ample scope for the implementation of AI in software testing, and this scope stems from its relevant benefits like:

  • Accuracy: Humans are prone to errors and software testing being a manual and iterative process, experts testers too tend to make small mistakes which might have far-reaching consequences. However, the implementation of the AI framework facilitates better handling of repetitive tasks leaving the testers free to concentrate on other essential tasks.
  • Limitless: Executing a web application testing by simulating or emulating hundreds of users can prove to be quite a daunting task for the tester. But AI provides ample scope for smooth implementation of test scripts to test web apps across innumerable users. Thus the automated testing process becomes even more robust, continuous and precise.
  • Overall test coverage improves: The implementation of AI increases the depth and scope of the testing process by quickly testing if the software meets the desired expectations. Hence, file content, database and the actual working of the test software can be analyzed and assessed even when thousands of tests are being implemented in a single run. The use of machine learning or ML; a subset of AI, helps to enhance testing tools for the applied algorithms automatically. This is done by collecting massive amounts of test generated data.
  • The application enhances the testing ability of the automation tools and enables them to make decisions regarding the tests that need to be run based on the changing data.
  • Quicker feedback: The faster the feedback is obtained from the tests conducted, the lesser time it takes to correct mistakes. Thus software testing takes lesser time and delivery schedule can be easily maintained because testers can effectively and efficiently identify controls and also keenly observe the connection present between the components and the defects observed.
  • QA boosting: AI can work throughout the year and can execute innumerable tests as and when required. Thus it becomes easier for the QA team to include prioritized testing, enhance automation, optimize test cases, reduce the analysis of mundane tasks, improve UI testing and analyze results accurately so that they can be communicated to the involved stakeholders. This makes the process trustworthy and also ensures the delivery of better quality products to the customers.
  • Time and money-saving: Since every test conducted requires the test code to be modified accordingly, manual rewriting of these codes consume much time. But AI can run tests at a faster pace without having to update the codes manually. This helps to reduce the testing lifespan considerably. Thus the implementation of AI helps sane money and time.
  • Integrated platform: The possibility of smooth execution of the developed software at the client site becomes a reality with the implementation of the AI framework since it enables the whole testing to be done on an embedded and integrated platform.

Thus it would not be wrong to say that AI with the help of machine learning has the capability to:

  • Write its own test script code as and when required by stealing from the existing software,
  • Perform test execution and also maintenance,
  • Smarter and faster regression testing and test automation
  • Facilitate faster and quicker identification of bugs etc.

Current trends of AI in software testing

Currently, AI and ML are being used to bridge gaps present between the software testing capabilities offered by humans and those that are machine-driven. Thus there is a high demand for AI-driven automation tools for testing. The development of AI facilitated automation tools are currently centered on:

  • AI-driven testing which focuses on developing AI tools for software testing,
  • Testing AI systems wherein methods to test the utility of AI systems are devised and manipulated and
  • Self-testing systems which result in the self-testing and self-heating of the software developed.

Today AI uses test bots who are intelligent autonomous agents. They help to automate such activities like discovery, test generation, modelling and detection of errors. These test bots are implemented by using machine learning or ML techniques. But they are not restricted to neural networks or reinforcement learning and decision tree learning. Today test bots are robust and are capable of acting even under uncertain conditions unlike the traditions automation test tools in vogue till date.

Some of the commons AI-driven automation testing approaches that have been made till date include:

  • In differential testing, different versions of the same application are tested and compared for understanding their differences and overbuilds and learning from them,
  • Visual testing wherein the new software application’s look and feel can be tested using screen comparisons and image-based learning,
  • Declarative testing which specifies the test intent in a domain-specific or natural language and lets the system figure out how best to carry out the same and
  • Self-healing automation deals with UI changing auto-correcting test element selection.

Future trends of AI in software testing

The future will see the AI framework being used to enhance the utility and functionality of the existing tools and also target some specific testing problems like:

  • Web and mobile apps functional testing,
  • User interface visual testing,
  • Selectors of autocorrecting elements and
  • Location of UI element.

Thus it would not be unusual to see Ai replacing almost all automated testing technology stacks and also take over all tasks which needed human intervention previously. Error analysis and its management are also expected to gain additional precision, and problems requiring more in-depth content are all set to be dealt with by AI in the future.

AI has a bright future where software testing is concerned provided; it can be made to co-exist peacefully with humans. Only then would it be possible for AI to improve on the process of software testing by removing bottlenecks and hassles currently being faced. Moreover, we would be able to bring in scalability, management documentation, security and performance; features which the AI lacks. Thus both can be made to work in tandem, thereby ensuring that the future of software testing shines brighter.

About the Author

QA InfoTech

QA InfoTech

Established in 2003, with less than five testing experts, QA InfoTech has grown leaps and bounds with three QA Centers of Excellence globally; two of which are located in the hub of IT activity in India, Noida, and the other, our affiliate QA InfoTech Inc Michigan USA. In 2010 and 2011, QA InfoTech has been ranked in the top 100 places to work for in India.

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