Augment your Quality Engineering process with AI and ML

Automation in software testing has been around for quite some time now. With the increasing expectations for faster releases and quick updates, manual software testing no longer cuts it. Therefore, organizations are shifting toward an automated way of software development and testing. 

Taking a look at the traditional automated software testing processes in an SDLC gives rise to the realization that it is not generating the desired outcomes in exchange for the investment that has been put into it. The primary reason for this emerged to be that organizations were still following a waterfall software development methodology in which software testing came at the end. After recognizing the need to shift the QA process early in the software development lifecycle, organizations started to embrace what is called Quality Engineering. 

Software quality engineering is all about shifting the focus from checking quality at the end to ensuring that quality is built into the code while it is being developed. Having software testing run parallel to the development process, with the help of automation, allowed the organizations to get rid of the obstacles posed by the legacy software development methodologies. 

Quality engineering practice driven by automation is although a comparatively efficient way of accelerating the speed to market and keep with the changing customer demands, there is still a huge room for further improvement. For example, to automate the test cases, a large chunk of time has to be invested into identifying, prioritizing, and authoring the test cases. This process sometimes takes longer than the actual development itself. Therefore, the need for a more efficient and quicker way to implement automation arises, which brings Artificial Intelligence and Machine Learning into the picture. 

Introducing cognitive capabilities into quality engineering 

By keeping a forward-looking outlook, we can expect to have the complete suite of cognitive computing interjected within a Quality Engineering lifecycle, which would include technologies like deep learning, self healing, and Natural Language Processing, in addition to Artificial Intelligence and Machine Learning. 

The introduction of AI into quality engineering allows the automation processes to do the heavy lifting related to the overall test management, while the manual professionals get the bandwidth to explore creative methods for improving the end quality. 

The present market dynamics have necessitated the implementation of a combined Agile+DevOps approach for SDLC. While Agile brings in the required speed, DevOps promotes the culture of collaboration and eliminates inter-departmental silos. The CI/CD pipeline established with such methodologies help streamline and accelerate the development and release process. However, there is often a lack of formal metrics for measuring 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 keeping a proper track of the KPIs and the metrics that need to be measured. 

The smart, cognitive capabilities of AI and ML algorithms allow the organizations to take a defect prediction approach rather than a defect prescription approach. This means, with time the algorithms are able to predict the areas where a defect may occur and enable the developers to fix them proactively. Taking a predictive, instead of a prescriptive approach, saves considerable time in the overall SDLC by reducing the need for the constant back and forth between dev and QA for defect detection and defect fixing. 

Further, AI and ML algorithms can be leveraged to automate the functional and non-functional aspects of software testing along with the test data environment and test suite optimization. By digitizing the release workflow and automation of the metrics measurement, the cognitive technologies optimize the release orchestration for improved efficiency. In test environment management and test data management, processes like provisioning, monitoring, and scheduling can be automated. 

Scriptless test automation 

Test script maintenance, being the most time-consuming and challenging aspect of test automation, can benefit significantly from AI-driven scripting of test cases. 

In scriptless test automation, manual efforts required to author a test case are reduced considerably. Testers are simply required to indicate the steps involved in writing an actual test case, and then AI algorithms can take it from there by translating the steps into the final test cases. The machine learning algorithms play an integral role in the continuous monitoring and maintenance of the test cases with the changing requirements. 

Also read: 6 benefits of shifting to Scriptless Test Automation 

A scriptless test automation framework can prove to be a game changer when it comes to a quality engineering SDLC, and therefore makes for a good candidate for AI and ML-driven capabilities. 

How can we help 

Cigniti’s Quality Engineering services ensure that testing shifts left and begins way ahead in the overall SDLC, ensuring maximum test coverage and quality. We achieve this with a strategic and result-oriented approach that automates and integrates the entire landscape for seamless functioning, and a comprehensive Digital Assurance & Testing strategy that provides scalable, reusable assets and enablers for improving the overall efficiency of Quality Assurance and Testing processes. 

Cigniti’s Quality Engineering services cover the Software Testing Life cycle, Test Consulting and Test Advisory services, Test Implementation, and Managed Testing services including Test Environment Management and Test Data Management. Leveraging process frameworks, methodologies, and tools, we help customers across various industries achieve first-time-right solution releases, quality improvements, and deliver a superior customer experience. 

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