Using data analytics techniques in software quality assurance

Abstract

Software quality assurance is a set of activities that ensures the quality of the software from different aspects. This process continues in parallel with the development of the software to ensure the quality of all processes, documentation, and the final software product. The focus of every company is to deliver the best quality product per the customer's needs and expectations. In that context, they try to minimize defect leakage and emphasize delivering a bug-free software product. However, covering 100 % software testing and delivering bug-free software is challenging. At the same time, it is possible to reduce the leakage to a minimal level with the help of best practices and modern techniques. In the above context, we try to understand the challenges faced by the companies to prevent the defects from leaking to the customer end. Additionally, what are the available modern data analytics techniques and practices that can help to reduce the number of defects in the final product? A deep understanding and review of these modern techniques will help the software quality specialists to include them in their quality process to modernize their working methods per the contextual need.

The research objective will be met by getting a deeper understanding of available techniques and providing a literature review to highlight an unbiased judgment.

The expected outcomes would be (i) a minimal set of test types and test levels to highlight the defects at earlier stages of SDLC (ii) a multivocal literature review on the inclusion of data analytics techniques in the quality assurance process (iii) An idea of a decision-making framework for prevention of defect leakage using analytics techniques for the future research.