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Multi-Objective Regression Test Selection

12 pagesPublished: March 1, 2021


Regression testing is challenging, yet essential, for maintaining evolving complex soft- ware. Efficient regression testing that minimizes the regression testing time and maximizes the detection of the regression faults is in great demand for fast-paced software develop- ment. Many research studies have been proposed for selecting regression tests under a time constraint. This paper presents a new approach that first evaluates the fault detectability of each regression test based on the extent to which the test is impacted by the changes. Then, two optimization algorithms are proposed to optimize a multi-objective function that takes fault detectability and execution time of the test as inputs to select an optimal subset of the regression tests that can detect maximal regression faults under a given time constraint. The validity and efficacy of the approach were evaluated using two empirical studies on industrial systems. The promising results suggest that the proposed approach has great potential to ensure the quality of the fast-paced evolving systems.

Keyphrases: multi-objective optimization, program invariant, Selective Regression Testing

In: Alexander Redei, Rui Wu and Frederick C. Harris Jr (editors). SEDE 2020. 29th International Conference on Software Engineering and Data Engineering, vol 76, pages 105--116

BibTeX entry
  author    = {Yizhen Chen and Mei-Hwa Chen},
  title     = {Multi-Objective Regression Test Selection},
  booktitle = {SEDE 2020. 29th International Conference on Software Engineering and Data Engineering},
  editor    = {Alex Redei and Rui Wu and Frederick Harris},
  series    = {EPiC Series in Computing},
  volume    = {76},
  pages     = {105--116},
  year      = {2021},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/7z5n}}
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