Automation in software testing has been around for quite some time now. With the rising expectations for quicker releases and fast updates, manual software testing no longer cuts it. Therefore, organizations are turning to an automated way of software development and testing.
Taking a glance at the traditional automated software testing processes in an SDLC gives rise to the understanding that it is not generating the desired results in exchange for the investment that has been put into it. The principal reason for this appeared to be that organizations were still using a waterfall software development methodology in which software testing came at the end. After realizing the need to shift the QA process early in the software development lifecycle, organizations began to embrace what is called Quality Engineering.
Quality engineering practice operated by automation is although a relatively efficient way of stimulating the speed to market and keep with the growing customer demands, there is still a large room for further improvement. For instance, to automate the test cases, a huge chunk of time has to be spent in identifying, prioritizing, and authoring the test cases. This process seldom takes longer than the actual development itself. Therefore, the requirement for a more efficient and quicker way to perform automation arises, which draws Artificial Intelligence and Machine Learning into the picture.
Introducing cognitive capabilities into quality engineering
The introduction of AI into quality engineering enables the automation processes to do the hefty lifting related to the overall test administration, while the manual professionals know the bandwidth to explore creative ways for enhancing the end quality.
The present market dynamics have constrained the implementation of a combined Agile+DevOps way for SDLC. While Agile brings in the requisite speed, DevOps promotes the culture of collaboration and reduces inter-departmental silos. The CI/CD pipeline built with such methodologies helps streamline and stimulate the development and release process. However, there is often a shortage of formal metrics for estimating the performance and functionalities of the releases.
AI and ML-driven Quality Engineering can result in optimization and acceleration of application quality and delivery speed while maintaining a proper track of the KPIs and the metrics that require to be measured.
The smart, cognitive capabilities of AI and ML algorithms enable the organizations to take a defect prediction approach rather than a defect prescription plan. This suggests, with time the algorithms are able to predict the regions where a defect may occur and allow the developers to set them proactively. Taking a predictive, rather than a prescriptive approach, saves considerable time in the overall SDLC by decreasing the need for the constant back and forth between dev and QA for error detection and error fixing.
Further, AI and ML algorithms can be used to automate the functional and non-functional features of software testing simultaneously with the test data environment and test suite optimization. By digitizing the release workflow and automation of the metrics computation, the cognitive technologies optimize the release orchestration for enhanced efficiency. In test environment administration and test data administration, processes like provisioning, monitoring, and scheduling can be automated.
Scriptless test automation
Test script maintenance, being the most time-consuming and challenging features of test automation, can help significantly from AI-driven scripting of test cases.
In scriptless test automation, manual efforts needed to author a test case are decreased considerably. Testers are simply needed to indicate the steps included in writing an actual test case, and then AI algorithms can get it from there by transposing the steps into the final test cases. The machine learning algorithms perform an integral role in the continuous monitoring and maintenance of test cases with dynamic requirements.
A scriptless test automation framework can prove to be a game-changer when it gets to a quality engineering SDLC, and therefore becomes a good candidate for AI and ML-driven abilities.
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