A systematic review on regression test selection techniques

Regression testing is verifying that previously functioning software remains after a change. With the goal of finding a basis for further research in a joint industry-academia research project, we conducted a systematic review of empirical evaluations of regression test selection techniques. We identified 27 papers reporting 36 empirical studies, 21 experiments and 15 case studies. In total 28 techniques for regression test selection are evaluated. We present a qualitative analysis of the findings, an overview of techniques for regression test selection and related empirical evidence. No technique was found clearly superior since the results depend on many varying factors. We identified a need for empirical studies where concepts are evaluated rather than small variations in technical implementations

Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review

Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration (CI) in software projects, which increases the frequency of running software builds, running all tests can be time-consuming and resource-intensive. To alleviate that problem, Test case Selection and Prioritization (TSP) techniques have been proposed to improve regression testing by selecting and prioritizing test cases in order to provide early feedback to developers. In recent years, researchers have relied on Machine Learning (ML) techniques to achieve effective TSP (ML-based TSP). Such techniques help combine information about test cases, from partial and imperfect sources, into accurate prediction models. This work conducts a systematic literature review focused on ML-based TSP techniques, aiming to perform an in-depth analysis of the state of the art, thus gaining insights regarding future avenues of research. To that end, we analyze 29 primary studies published from 2006 to 2020, which have been identified through a systematic and documented process. This paper addresses five research questions addressing variations in ML-based TSP techniques and feature sets for training and testing ML models, alternative metrics used for evaluating the techniques, the performance of techniques, and the reproducibility of the published studies