Why AI is so important in optimizing Software Quality Assurance
Digital Transformation is making a huge business impact. This is mainly driven by the advent of new technologies, development/adoption of new products or business models, and growing customer expectations. The increasingly competitive nature of online business has led enterprises to aim at garnering more customers to realize the ROI.
As the growing competition has resulted in new product launches, the need to validate their quality through software quality assurance has become important. Faced with such constraints including that of time and cost, enterprises look to leverage Artificial Intelligence and machine learning to understand the needs of customers based on their past behavioural patterns.
Moreover, the spread of Internet of Things (IoT) and its growing scope have increased the need for testing the IoT processes, embedded devices and the software within them. For in the absence of any software quality assurance vis-a-vis IoT, the devices would falter in their functioning leading to unpredictable outcomes. However, the use of traditional methods for testing embedded devices can be time consuming leading to a bad user experience. This is where AI and machine learning can help in predicting the user traffic, delivering real time insights and bring about a drastic improvement in user experience.
As enterprises deal with big data, they look to leverage the same to deliver productive outcomes. The use of data mining and predictive analytics a la AI can help businesses to optimize their processes, predict customer behaviour and create better products. One of the foremost challenges of Agile and DevOps based test automation is identifying processes for software quality assurance testing. This is due to the fact that not all processes can be automated owing to process complexity, lack of time and resources to write the script, and cost. AI and machine learning can help in identifying processes to be automated based on repetitive patterns of data. The use of AI and machine learning can help improve the test efficiencies and enable better decision making.
AI can optimize software quality assurance in the following ways:-
Analysing the defects: One of the main objectives of Agile and DevOps based testing is identifying and eliminating glitches early in the design and development phase. AI can help in identifying the most critical glitches as opposed to the lesser ones. Thus, the QA software testing team can prioritize regression testing leading to quicker turnaround times.
Analysing Customer behavioural patterns: The testers can leverage the use of monitors or embedded sensors to generate behavioural patterns. These patterns throw light on the demographics, geographies, and devices of users. The same can be used by testers to build better test suites for achieving a greater test efficiency.
Use of social media: Effective test suits can be built by analyzing suitable data patterns from social media usage of customers. This is in terms of identifying the demographic trends.
Enhancing QA testing: AI can help in optimizing test cases, prioritizing testing, and reducing the task of analyzing complex data patterns. Moreover, by mining the test management data from dashboard, the productivity of testers can be ascertained. The productivity is related to the creation and execution of test cases. The knowledge can be used to bring efficiency in the testing process leading to faster test outcomes.
Non functional analysis: AI can help in generating performance reports, identifying security vulnerabilities and SLA misses based on the operational dashboard. The information can be used to plug security vulnerabilities, reduce performance issues and meet SLA targets.
Better feedback: QA testers can get a better and quicker feedback through AI. The analytics element in AI can run test cases quickly by mimicking the input test scenarios. These scenarios can be very tedious given their repetitive and complex nature. AI can generate a suitable output from such scenarios based on past data patterns.
Predictive analytics: Test automation leads to the generation of vast amounts of data (read big data.) The big data can form the basis of identifying and forecasting the quality of processes and products. As a consequence of identifying the test outcomes or inherent glitches in the system, AI can guide the tester by prescribing a better course of action.