Open AccessMulti-Objective Regression Test Selection
Author(s) -
Yizhen Chen,
Mei-Hwa Chen
Publication year - 2021
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.21
H-Index - 7
ISSN - 2398-7340
DOI - 10.29007/7z5n
Subject(s) - regression testing , computer science , regression , regression analysis , constraint (computer aided design) , data mining , fault (geology) , fault detection and isolation , machine learning , artificial intelligence , software , statistics , engineering , mathematics , software system , mechanical engineering , software construction , seismology , actuator , programming language , geology
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.