Selective Regression Testing of Safety-Critical Systems: A Black Box Approach

Andrews, Anneliese; Elakeili, Salwa; Alhaddad, Ahmed

doi:10.1109/qrs-c.2015.16

Cited by 2 publications

(2 citation statements)

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“…Korel et al used an extended finite‐state machine model dependence analysis for identifying modifications . Andrews et al applied model‐based selective regression testing to a railroad crossing control system based on the behavioral model of a system and a behavioral test suite . However, a common limitation of model‐based regression testing techniques is the ambiguous quality of regression test suites, because these techniques use only a modified system model instead of an original .…”

Section: Related Workmentioning

confidence: 99%

“…31 Andrews et al applied model-based selective regression testing to a railroad crossing control system based on the behavioral model of a system and a behavioral test suite. 32 However, a common limitation of model-based regression testing techniques is the ambiguous quality of regression test suites, because these techniques use only a modified system model instead of an original. 31 Another limitation of these approaches is that they give limited attention to the problem of regression testing of HCS developed in CI, especially in the case of highly interleaved tests.…”

Section: Related Workmentioning

confidence: 99%

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A learning algorithm for optimizing continuous integration development and testing practice

2018

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Summary Continuous integration, at its core, includes a set of practices that aim to prevent and reduce the cost of software integration issues by merging working software copies often. Regression testing is considered a good practice in software development with continuous integration, which ensures that code changes are not negatively affecting software functionality. As, nowadays, software development is carried out iteratively, with small code increments continuously developed and regression tested, it is of critical importance that continuous regression testing is time efficient. However, in practice, regression testing is often long lasting and faces scalability problems as software grows larger or as software changes are made more frequently. One contributing factor to these issues is test redundancy, which causes the same software functionality being tested multiple times across a test suite. In large‐scale software, especially highly configurable software, redundancy in continuous regression testing can significantly grow the size of test suites and negatively affect the cost effectiveness of continuous integration. This paper presents a practical learning algorithm for optimizing continuous integration testing by reducing ineffective test redundancy in regression suites. The novelty of the algorithm lies in learning and predicting the fault‐detection effectiveness of continuous integration tests using historical test records and combining this information with coverage‐based redundancy metrics. The goal is to identify ineffective redundancy, which is maximally reduced in the resulting regression test suite, thus reducing test time and improving the performance of continuous integration. We apply and evaluate the algorithm in two industrial projects of continuous integration. The results show that the proposed algorithm can improve the efficiency of continuous integration practice in terms of decreasing test execution time by 38% on average compared to the industry practice of our case study and by 40% on average compared to the retest‐all approach. The results further demonstrate no significant reduction in fault‐detection effectiveness of continuous regression testing. This suggests that the proposed algorithm contributes to the state of the practice in the continuous integration development and testing of highly configurable systems.

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Section: Related Workmentioning

confidence: 99%