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)

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
[1]TIAN T,GONG D W.Evolutionary generation approach of test data for multiple paths coverage of message-passing parallel programs[J].Chinese Journal of Electronics,2014,23(2):291-296.
[2]SAHIN O,AKAY B.Comparisons of metaheuristic algorithms and fitness functions on software test data generation[J].Applied Soft Computing,2016,49:1202-1214.
[3]PRADHAN D,WANG S,ALI S,et al.Employing rule mining and multi-objective search for dynamic test case prioritization[J].The Journal of Systems and Software,2019,153:86-104.
[4]WONG W,HORGAN J,LONDON S,et al.A study of effective regression testing in practice[C]//Proceedings of the Eighth International Symposium on Software Reliability Engineering.New Mexico,USA,1997,11:264-274.
[5]KAVITHA R,SURESHKUMAR N.Test case prioritization for regression testing based on severity of fault[J].International Journal on Computer Science & Engineering,2010,2(5):1462-1466.
[6]NAYAK S,KUMAR C,TRIPATHI S.Enhancing efficiency of the test case Prioritization technique by improving the rate of fault detection[J].Arabian Journal for Science & Engineering,2017,42(11):1-17.
[7]WALCOTT K R,SOFFA M L,KAPFHAMMER G M,et al.Time-Aware test suite prioritization[C]//Pollock L,ed.Proc.of the Int’l Symp.on Software Testing and Analysis.Portland:ACM Press,2006:1-12.
[8]SRIKANTH H,WILLIAMS L,OSBORNE J.System test case prioritization of new and regression test cases[C]//Proceedings of the International Symposium on Empirical Software Engineering.Noosa Heads,Australia,2005:64-73.
[9]CHEN X,CHEN J H,JU X L,et al.Survey of test case prioritization techniques for regression testing[J].Journal of Software,2013,24(8):1695-1712.
[10]MICHAEL R G,DAVID S J.Computers and intractability:a guide to the theory of NP-completeness[M].WH Free.Co,San Fr,1979.
[11]MAHMOOD H,HOSAIN S.Improving test case prioritization based on practical priority factors[C]//Proceedings of IEEE International Conference on Software Engineering and Service Science.Beijing:IEEE Press,2017:899-902.
[12]CHEN Y F,LI Z,ZHAO R L.Applying PSO to multi-objective test cases prioritization[J].Computer Science,2014,41(5):72-77.
[13]MUKHERJEE R,PATNAIK K S.A survey on different approaches for software test case prioritization[J].Journal of King Saud University Computer and Information Sciences,2018:1319-1578.
[14]COWLING P,KENDALL G,SOUBEIGA E.A hyperheuristic approach to scheduling a sales summit[C]//Practice and Theory of Automated Timetabling.2001:176-190.
[15]KENDALL G,SOUBEIGA E,COWLING P.Choice function and random hyperheuristics[C]//Asia-Pacific Conference on Simulated Evolution and Learning.Springer,2002:667-671.
[16]MAASHI M,ÖZCAN E,KENDALL G.A multi-objective hyper-heuristic based on choice function[J].Expert Syst.Appl.,2014,41(9):4475-4493.
[17]郑金华.多目标进化算法及其应用[M].北京:科学出版社,2007:2-8.
[18]ROTHEMEL G,UNTCH R H,CHU C,et al.Prioritiz-ing test cases for regression testing[J].IEEE Transactions on Software Engineering,2001,27(10):929-948.
[19]SUN C A,GUO X L,ZHANG X Y,et al.A Data Flow Analysis Based Redundant Mutants Identification Technique[J].Chinese Journal of Computers,2019,42(1):44-60.
[20]ZHENG Y,WANG Z,FANG X,et al.Localizing multiple software faults based on evolution algorithm[J].The Journal of Systems and Software,2018,139:107-123.
[21]YOO S,HARMAN M,CLARK D.Fault localization prioritization:comparing information theoretic and coverage based approaches[J].ACM Transactions on Software Engineering and Methodology,2013,22(3):1049-1078.
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