Abstract
In applied software engineering, the algorithms for selecting the appropriate test cases are used to perform regression testing. The key objective of this activity is to make sure that modification in the system under test (SUT) has no impact on the overall functioning of the updated software. It is concluded from the literature that the efficacy of the test case selection solely depends on the following metrics, namely, the execution cost of the test case, the lines of the code covered in unit time also known as the code coverage, the ability to capture the potential faults, and the code modifications. Furthermore, it is also observed that the approaches for the regression testing developed so far generated results by focusing on one or two parameters. In this paper, our key objectives are twofold: one is to explore the importance of the role of each metric in detail. The secondary objective is to study the combined effect of these metrics in test case selection task that is capable of achieving more than one objective. In this paper, a detailed and comprehensive review of the work related to regression testing is provided in a very distinct and principled way. This survey will be useful for the researchers contributing to the field of regression testing. It is noteworthy that our systematic literature review (SLR) included the noteworthy work published from 2007 to 2020. Our study observed that about 52 relevant studies focused on all of the four metrics to perform their respective tasks. The results also revealed that about 30% of the different categories of regression test case reported the results using metaheuristic regression test selection (RTS). Similarly, about 31% of the literature reported results using the generic regression test case selection techniques. Most of the researchers focus on the datasets, namely, Software-Artefact Infrastructure Repository (SIR), JodaTime, TreeDataStructure, and Apache Software Foundation. For validation purpose, following parameters were focused, namely, the inclusiveness, precision, recall, and retest-all.
Highlights
Muhammad Rehan,1 Norhalina Senan,1 Muhammad Aamir,2 Ali Samad,3 Mujtaba Husnain,3 Noraini Ibrahim,1 Sikandar Ali,4 and Hizbullah Khatak 5
We try to explore and identify the set of techniques that are capable of producing the efficient and acceptable results based on these effective measures. e primary objective of this paper is to explore the test case selection methods that perform solely on these metrics and work as an effective contributor. e second objective our study is to assess the current state-of-the-art regression test case selection frameworks and techniques developed so far and to identify the available datasets and algorithms for the solution of test case selection problems
In the literature reviewed in this article, it is observed that most of the selection methods focus their tabs on the application of some specific software domain that limits our effort in retrieving any significant evidence to analyze and assess the superiority and dominancy of one method over the other
Summary
We will discuss the methods that we used to retrieve the related articles. Some smaller node values like “the regression test case,” “test case selection method,” and “adaptive random testing” are of key importance while searching the research articles of the same kind These small clusters are most likely to be the hot topics in near future in the domain of software testing. E average number of publications is shown by the time span on year scaling, representing that the papers that were published recently will appear on the front, indicating the emerging trends in the cluster It is observed that the recent trends that were exercised in the field of software
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