Does Ai Play A Critical Role In Building Future Qa Strategies?

By Author: Oliver Moore
Total Articles: 52
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Enterprises embark upon digital transformation to achieve a host of outcomes including improved productivity, better customer experience, efficiency, cost savings, and enhanced ROI. In the quest to modernize the IT infrastructure, enterprises often give Quality Assurance or QA the miss. However, with Agile and DevOps methodologies focusing on delivering quality products or services within shorter release cycles, QA invariably slips into any enterprise’s development strategy. The pressure of delivering shorter release cycles has necessitated the adoption of test automation, for the traditional model is no longer found to be adequate.

Does that make the execution of test automation a breeze? Far from it. Challenges arise from the multiplicity of device platforms, operating and network environments, and the advent of technologies like IoT, big data, cloud computing, and others. So, what is the way out? How to ensure the QA process delivers foolproof codes and the resultant products or services be free of glitches or vulnerabilities? Embark on AI-based testing. Does this sound inscrutable? Well, AI has already forayed into ...
... our daily lives in the form of Siri and Alexa smart personal assistants. Also, the World Quality Report emphasizes the need to employ machine-based intelligence solutions to overcome QA and testing challenges and execute risk-based strategies. Hence, it has become common knowledge that AI-based testing can render QA for test automation and regression testing smarter, better, and faster.

Do AI and ML sound the death knell for software testing?

The answer is a big NO, for AI and ML can enhance the quality of software testing by making the process of identifying bugs quicker. However, notwithstanding the advantage of AI-based testing over humans when it comes to running test automation cases and delivering precise results, humans still hold the key to aspects such as scalability, performance, and others.

How can AI and ML shake things up in QA

The centrality of continuous integration and delivery in the DevOps scheme of things means QA, comprising test automation and manual processes, gets a leg-up. This can only happen by implementing AI test automation. Let us discuss the benefits AI can accrue to the existing QA processes.

•Accelerating manual testing and other processes: Manually testing a set of software codes for performance can take days on end. Besides, manual testing can be expensive both in terms of money and time. However, AI can do away with such tests by writing the scripts and analyzing large quantum of data, faster. It can easily sort through the log files to enhance the quality of the codes. Notwithstanding the benefits, AI cannot replace traditional manual testing as the latter is required to design the testing strategies. The future is about the co-existence of both manual and artificial intelligence testing services.

•Test automation to the fore: As software development becomes complex with APIs interfacing with myriad touchpoints, the chances of glitches to scrape through the build and test processes are immense. This is where test automation can push the envelope by checking umpteen test cases for different scenarios and comprising myriad variables. Test automation can facilitate regression testing and offer greater efficiency by identifying a large number of glitches in less time. So, in a DevOps-driven build and test environment requiring continuous integration and testing, test automation can be the best bet for QA testers. However, with test automation to the fore, the requirement for creating test scripts with a reporting mechanism can be a challenge. This is where AI testing services can help write complex test cases quickly.

•Defect prevention: AI app testing services can study log reports containing historical data on glitches and guide developers about the areas where the likelihood of the presence of glitches can be more. These can combine the best of approaches to deliver superior results that are three dimensional in their scope. The three dimensions include eliminating the overlap of test coverage, generating more predictable testing outcomes, and moving from the detection of defects to their prevention. By analyzing patterns and processing huge quantum of data, developers and QA experts can take better decisions in real time. For example, machine learning algorithms can analyze a set of codes during a software upgrade to identify the key changes in functionality. The algorithms can easily link the changes to the test cases and optimize the QA process.

•Bug elimination: Even with the best of intentions and by following proper protocols, the software codes can often end up with bugs. Identifying them can be a nightmare even with test automation, as it can only help in bug detection if the built-in script facilitates the same. On the other hand, employing an AI testing framework can get answers like where, how, and when within seconds. The answers can help developers to understand whether they need to make coding changes to prevent such errors or apply other approaches. In fact, AI can execute real time analysis of glitches while a code is being developed.

•Impact analysis: AI testing services can analyze the impact of glitches in software without the involvement of QA experts. They can sift the relationship between various elements within software should there be glitches. AI can help the QA specialists and development team in prioritizing glitches so that they can go about addressing them, including the anomalies in their processes that have led to the glitches to appear in the first place.

Conclusion

The QA specialists and developers can effectively utilize Artificial Intelligence or AI to predict the emergence of glitches or forming an effective triage in tackling them. The technology can be more than handy in ensuring the software of the future remains qualitatively superior.

Author Bio
Oliver has been associated with Cigniti Technologies Ltd as an Associate Manager - Content Marketing, with over 10 years of industry experience as a Content Writer in Software Testing & Quality Assurance industry. Cigniti is a Global Leader in Independent Quality Engineering & Software Testing Services with CMMI-SVC v1.3, Maturity Level 5.