Enterprises operating in the digital realm can achieve success if they ensure their software applications are updated as per the latest trends and customer preferences. This forces the IT teams to be faster and agile, and adopt measures to align their work culture to meet the demands of the business. No wonder they are adopting Agile and DevOps methodologies to speed up software development and delivery besides fostering collaboration. Agile and DevOps have quality assurance embedded in them leading to continuous testing of the code build.
Continuous testing ensures that any change in code is implemented instantaneously and effectively.
It also places iterative QA across the SDLC thereby accelerating the build quality. However, continuous testing in the omnichannel environment can be riddled with issues such as a lack of visibility of requirements across processes, siloed automation, and a high volume of tests, among others. To address these issues and drive quality at speed across the SDLC, it is important to adopt an AI-based testing approach.
How can AI facilitate continuous testing?
Artificial Intelligence can remove manual intervention and smoothen the process of continuous testing. In AI testing, QA teams can initiate the test cycle to identify and fix bugs based on insights drawn from past events and historical data sets. AI-driven testing ensures that only a robust code is moved across the SDLC thereby foregrounding quality as the centrepiece of software development. Even though the testing activities are automated in continuous testing, the code needs to be signed off manually to move across quality gates based on the test results. This siloed automation can disrupt the flow of code in the SDLC. However, with AI test automation, the code checking at quality gates is done automatically and code progression is managed by analyzing the test results. This makes the workflow fully automated. By triggering QA processes with artificial intelligence, the testing teams can achieve the following:
Automation of quality gates: Given that the AI algorithm can determine the course of code progression based on historical data, the testing teams can allow/entrust the AI testing services with facilitating code progression. Conversely, the AI engine can prevent code progression if it finds a high probability of outage or disruption to be caused by the presence of bugs.
Identification of root causes: AI-based testing can analyze the patterns and correlations in a build to identify the root causes of defects. Thereafter, the AI engine can orchestrate remedial tests to reduce the margin of errors in the build.
Focus on brand assurance: With an AI testing framework, test automation is made all-pervasive and intelligent. This allows the QA teams to glean insights from historical data, business acumen, and customer inputs, thereby shifting the QA focus to brand assurance and competitiveness.
Predictive analysis: AI-based testing can analyze the existing customer data to understand how the user behavior and needs will evolve in future. These insights will give the software development and QA teams to come up with suitable features and functionalities to be ahead of the curve.
API test evaluation: The performance of a software application depends to a large extent on the quality of interactions between various modules connected with databases and servers, among others. By evaluating the performance of the APIs, the testers can validate if the connection between various modules in the application is stable. They also check if the requests are processed accurately and the customers will get the correct outcome post a specific interaction. Artificial intelligence led testing can analyze the functionality of the APIs and large volumes of data to detect risks and create precise test cases.
Scriptless test automation: Even though test automation has revolutionized the QA process, the writing of test scripts to automate various test processes can be a challenge. Besides, the scripts need to be maintained as well for further usage thereby escalating the cost. However, an AI testing framework can create test cases automatically leaving the QA specialists to focus on other areas of software testing. Often developers writing test cases do not have the time to explore additional possibilities for the test cases. Consequently, they may end up writing test cases they are familiar with rather than writing the ones that can generate the least redundant data. AI-driven testing can run a minimal number of tests to understand the kind of impact (positive or negative) a code change can entail. With limited manual intervention, the artificial intelligence led testing can ensure no redundant data and bottlenecks are there in the application.
Conclusion
Artificial Intelligence can open up new avenues for testing software applications. In a day and age where the speed and quality of applications go hand-in-hand to generate superior user experiences, reduce costs, and improve profitability, AI-based testing has become an enabler for enterprises to stay competitive.
