Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6): 38-43.doi: 10.11896/jsjkx.191100113

• Intelligent Software Engineering • Previous Articles     Next Articles

Test Case Prioritization Based on Multi-objective Optimization

XIA Chun-yan1, WANG Xing-ya2, ZHANG Yan1   

  1. 1 School of Computer and Information Technology,Mudanjiang Normal University,Mudanjiang,Heilongjiang 157011,China
    2 State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China
  • Received:2019-11-15 Online:2020-06-15 Published:2020-06-10
  • About author:XIA Chun-yan,born in 1980,postgra-duate,associate professor,is a member of China Computer Federation.Her main research interests include search-based software engineering,information processing and data mining.
    WANG Xing-ya,born in 1990,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include blockchain analysis and testing,software defect location.
  • Supported by:
    This work was supported by Science Research Project of Heilongjiang Provincial Education Department (1353MSYYB005) and Research Project of Mudanjiang Normal University (2018-KYYWF-0419)

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Abstract: Regression testing is the most frequently used and expensive testing method in software testing.Test case prioritization is an effective way to reduce the cost of regression testing.Its purpose is to improve the ability of software fault detection by prio-ritizing the execution of high-level test cases.In this paper,a method of test case prioritization based on multi-objective optimization is proposed.The method integrates choice function into individual evaluation mechanisms of genetic algorithm.By designing a reasonable coding method and appropriate selection,crossover and mutation strategies,taking fault detection rate,sentence covera-ge rate and effective execution time as optimization objectives,non-dominated sorting genetic algorithm is used to optimize test case sort.The experimental results based on four benchmark programs and four industrial programs show that the proposed method can improve the effectiveness of software testing compared with other methods.

Key words: Choice function, Multi-objective optimization, Non-dominated sorting genetic algorithm, Test case priority, Software testing

CLC Number: 

  • TP311
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