Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017). Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration. In Proceedings of 26th International Symposium on Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC

Using Fuzzy Logic in Test Case Prioritization for Regression Testing Programs with Assertions

Program assertions have been recognized as a supporting tool during software development, testing, and maintenance. Therefore, software developers place assertions within their code in positions that are considered to be error prone or that have the potential to lead to a software crash or failure. Similar to any other software, programs with assertions must be maintained. Depending on the type of modification applied to the modified program, assertions also might have to undergo some modifications. New assertions may also be introduced in the new version of the program, while some assertions can be kept the same. This paper presents a novel approach for test case prioritization during regression testing of programs that have assertions using fuzzy logic. The main objective of this approach is to prioritize the test cases according to their estimated potential in violating a given program assertion. To develop the proposed approach, we utilize fuzzy logic techniques to estimate the effectiveness of a given test case in violating an assertion based on the history of the test cases in previous testing operations. We have conducted a case study in which the proposed approach is applied to various programs, and the results are promising compared to untreated and randomly ordered test cases

Unifying regression testing with mutation testing

textSoftware testing is the most commonly used methodology for validating quality of software systems. Conceptually, testing is simple, but in practice, given the huge (practically infinite) space of inputs to test against, it requires solving a number of challenging problems, including evaluating and reusing tests efficiently and effectively as software evolves. While software testing research has seen much progress in recent years, many crucial bugs still evade state-of-the-art approaches and cause significant monetary losses and sometimes are responsible for loss of life. My thesis is that a unified, bi-dimensional, change-driven methodology can form the basis of novel techniques and tools that can make testing significantly more effective and efficient, and allow us to find more bugs at a reduced cost. We propose a novel unification of the following two dimensions of change: (1) real manual changes made by programmers, e.g., as commonly used to support more effective and efficient regression testing techniques; and (2) mechanically introduced changes to code or specifications, e.g., as originally conceived in mutation testing for evaluating quality of test suites. We believe such unification can lay the foundation of a scalable and highly effective methodology for testing and maintaining real software systems. The primary contribution of my thesis is two-fold. One, it introduces new techniques to address central problems in both regression testing (e.g., test prioritization) and mutation testing (e.g., selective mutation testing). Two, it introduces a new methodology that uses the foundations of regression testing to speed up mutation testing, and also uses the foundations of mutation testing to help with the fault localization problem raised in regression testing. The central ideas are embodied in a suite of prototype tools. Rigorous experimental evaluation is used to validate the efficacy of the proposed techniques using a variety of real-world Java programs.Electrical and Computer Engineerin

SOFTWARE UNDER TEST DALAM PENELITIAN SOFTWARE TESTING: SEBUAH REVIEW

Software under Test (SUT) is an essential aspect of software testing research activities. Preparation of the SUT is not simple. It requires accuracy, completeness and will affect the quality of the research conducted. Currently, there are several ways to utilize an SUT in software testing research: building an own SUT, utilization of open source to build an SUT, and SUT from the repository utilization. This article discusses the results of SUT identification in many software testing studies. The research is conducted in a systematic literature review (SLR) using the Kitchenham protocol. The review process is carried out on 86 articles published in 2017-2020. The article was selected after two selection stages: the Inclusion and Exclusion Criteria and the quality assessment. The study results show that the trend of using open source is very dominant. Some researchers use open source as the basis for developing SUT, while others use SUT from a repository that provides ready-to-use SUT. In this context, utilization of the SUT from the software infrastructure repository (SIR) and Defect4J are the most significant choice of researchers

Preemptive regression testing of workflow-based web services

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