Clustering based novel test case prioritization technique

Chaurasia, Geetanjali; Agarwal, Sonali; Gautam, Swarnima Singh

doi:10.1109/sces.2015.7506447

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…The downside of such an approach may be higher computational complexity. Other approaches include active learning for test classi cation [3], combining machine learning and program slicing for regression test case prioritization [41], learning agent-based test case prioritization [2], or clustering approaches [5]. RL has been previously used in combination with adaptationbased programming (ABP) for automated testing of software APIs, where the combination of RL and ABP successively selects calls to the API with the goal to increase test coverage, by Groce et al [15].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement learning for automatic test case prioritization and selection in continuous integration

Spieker

Gotlieb

Marijan

et al. 2017

Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis

197

176

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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 R , 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 R 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 R method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying R on data extracted from three industrial case studies, we show for the rst time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing. CCS CONCEPTS•Software and its engineering → Software veri cation and validation; Software testing and debugging;

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Section: Related Workmentioning

confidence: 99%

Reinforcement learning for automatic test case prioritization and selection in continuous integration

Spieker

Gotlieb

Marijan

et al. 2017

Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis

197

176

View full text Add to dashboard Cite

show abstract

“…G1: Increasing test suite's rate of fault detection 3,[6][7][8]17,[19][20][21]25,27,[29][30][31][32][33]35,35,36,42,[44][45][46][47][48][48][49][50]54,55,[62][63][64] Coverage-based information, 3,19,27,30,31,33,44,63 Requirements information, 6,21,33,62 Test execution/fault detection history 3,[6][7][8]20,27,31,33,…”

Section: Goal Information Need Metricmentioning

confidence: 99%

Using goal–question–metric to compare research and practice perspectives on regression testing

Minhas

Koppula

Petersen

et al. 2022

J Software Evolu Process

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Regression testing is challenging because of its complexity and the amount of effort and time it requires, especially in large-scale environments with continuous integration and delivery. Regression test selection and prioritization techniques have been proposed in the literature to address the regression testing challenges, but adoption rates of these techniques in industry are not encouraging. One of the possible reasons could be the disparity in the regression testing goals in industry and literature. This work compares the research perspective to industry practice on regression testing goals, corresponding information needs, and metrics required to evaluate these goals. We have conducted a literature review of 44 research papers and a survey with 56 testing practitioners. The survey comprises 11 interviews and 45 responses to an online questionnaire. We identified that industry and research accentuate different regression testing goals. For instance, the literature emphasizes increasing the fault detection rates of test suites and early identification of critical faults. In contrast, the practitioners' focus is on test suite maintenance, controlled fault slippage, and awareness of changes. Similarly, the literature suggests maintaining information needs from test case execution histories to evaluate regression testing techniques based on various metrics, whereas, at large, the practitioners do not use the metrics suggested in the literature. To bridge the research and practice gap, based on the literature and survey findings, we have created a goal-question-metric (GQM) model that maps the regression testing goals, associated information needs, and metrics from both perspectives. The GQM model can guide researchers in proposing new techniques closer to industry contexts. Practitioners can benefit from information needs and metrics presented in the literature and can use GQM as a tool to follow their regression testing goals.

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“…Among these categories, clustering-based methods have been employed by multiple researchers for TCP [82,83,84,85,86,27,87,88,89,90,65,91,62]. Clustering methods partition data points into groups or clusters, according to the similarity function between the data points, such that data points in the same group have high similarity.…”

Section: Similarities Of Test Casesmentioning

confidence: 99%

Test case prioritization using test case diversification and fault-proneness estimations

Mahdieh¹,

Mirian-Hosseinabadi²,

Mardi³

2021

Preprint

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Context: Regression testing activities greatly reduce the risk of faulty software release. However, the size of the test suites grows throughout the development process, resulting in time-consuming execution of the test suite and delayed feedback to the software development team. This has urged the need for approaches such as test case prioritization (TCP) and test-suite reduction to reach better results in case of limited resources. In this regard, proposing approaches that use bug history can be interesting.Objective: Our aim is to propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. To evaluate this approach we study its performance on real-world open-source projects.Method: The bug history is used to estimate the fault-proneness of source code areas. The diversification of test cases is preserved by incorporating fault-proneness on a clustering-based approach scheme.Results: The proposed methods are evaluated on datasets collected from the development history of five real-world projects including 357 versions in total. The experiments show that the proposed methods are superior to coverage-based TCP methods. Conclusion:The proposed approach shows that improvement of coverage-based and fault-proneness based methods is possible by using a combination of diversification and fault-proneness incorporation.

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Clustering based novel test case prioritization technique

Cited by 8 publications

References 10 publications

Reinforcement learning for automatic test case prioritization and selection in continuous integration

Reinforcement learning for automatic test case prioritization and selection in continuous integration

Using goal–question–metric to compare research and practice perspectives on regression testing

Test case prioritization using test case diversification and fault-proneness estimations

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