On guiding the augmentation of an automated test suite via mutation analysis

被引:0
|
作者
Ben H. Smith
Laurie Williams
机构
[1] North Carolina State University,Department of Computer Science
来源
关键词
Mutation testing; Line coverage; Fault injection; Empirical effectiveness; Test case augmentation; Mutation analysis; Mutation testing tool; Statement coverage; Test adequacy; Web application; Open source; Unit testing;
D O I
暂无
中图分类号
学科分类号
摘要
Mutation testing has traditionally been used as a defect injection technique to assess the effectiveness of a test suite as represented by a “mutation score.” Recently, mutation testing tools have become more efficient, and industrial usage of mutation analysis is experiencing growth. Mutation analysis entails adding or modifying test cases until the test suite is sufficient to detect as many mutants as possible and the mutation score is satisfactory. The augmented test suite resulting from mutation analysis may reveal latent faults and provides a stronger test suite to detect future errors which might be injected. Software engineers often look for guidance on how to augment their test suite using information provided by line and/or branch coverage tools. As the use of mutation analysis grows, software engineers will want to know how the emerging technique compares with and/or complements coverage analysis for guiding the augmentation of an automated test suite. Additionally, software engineers can benefit from an enhanced understanding of efficient mutation analysis techniques. To address these needs for additional information about mutation analysis, we conducted an empirical study of the use of mutation analysis on two open source projects. Our results indicate that a focused effort on increasing mutation score leads to a corresponding increase in line and branch coverage to the point that line coverage, branch coverage and mutation score reach a maximum but leave some types of code structures uncovered. Mutation analysis guides the creation of additional “common programmer error” tests beyond those written to increase line and branch coverage. We also found that 74% of our chosen set of mutation operators is useful, on average, for producing new tests. The remaining 26% of mutation operators did not produce new test cases because their mutants were immediately detected by the initial test suite, indirectly detected by test suites we added to detect other mutants, or were not able to be detected by any test.
引用
收藏
页码:341 / 369
页数:28
相关论文
共 50 条
  • [31] Instructor-Written Hints as Automated Test Suite Quality Feedback
    Perretta, James
    DeOrio, Andrew
    Guha, Arjun
    Bell, Jonathan
    PROCEEDINGS OF THE 56TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE TS 2025, VOL 2, 2025, : 910 - 916
  • [32] Instructor-Written Hints as Automated Test Suite Quality Feedback
    Perretta, James
    DeOrio, Andrew
    Guha, Arjun
    Bell, Jonathan
    PROCEEDINGS OF THE 56TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE TS 2025, VOL 2, 2025,
  • [33] Automated Test Suite Generation for Time-continuous Simulink Models
    Matinnejad, Reza
    Nejati, Shiva
    Briand, Lionel C.
    Bruckmann, Thomas
    2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), 2016, : 595 - 606
  • [34] SolAR: Automated test-suite generation for solidity smart contracts
    Driessen, S. W.
    Di Nucci, D.
    Tamburri, D. A.
    Van den Heuvel, W. J.
    SCIENCE OF COMPUTER PROGRAMMING, 2024, 232
  • [35] Instructor-Written Hints as Automated Test Suite Quality Feedback
    Perretta, James
    DeOrio, Andrew
    Guha, Arjun
    Bell, Jonathan
    PROCEEDINGS OF THE 56TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE TS 2025, VOL 1, 2025, : 1120 - 1126
  • [36] Instructor-Written Hints as Automated Test Suite Quality Feedback
    Perretta, James
    DeOrio, Andrew
    Guha, Arjun
    Bell, Jonathan
    PROCEEDINGS OF THE 56TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE TS 2025, VOL 2, 2025,
  • [37] Instructor-Written Hints as Automated Test Suite Quality Feedback
    Perretta, James
    DeOrio, Andrew
    Guha, Arjun
    Bell, Jonathan
    PROCEEDINGS OF THE 56TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE TS 2025, VOL 1, 2025, : 1071 - 1077
  • [38] Genetic Algorithm for Automatic Generation of Representative Test Suite for Mutation Testing
    Rao, C. Prakasa
    Govindarajulu, P.
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2015, 15 (02): : 11 - 17
  • [39] Test suite minimization for mutation testing of WS-BPEL compositions
    Palomo-Lozano, Francisco
    Estero-Botaro, Antonia
    Medina-Bulo, Inmaculada
    Nunez, Manuel
    GECCO'18: PROCEEDINGS OF THE 2018 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2018, : 1427 - 1434
  • [40] Mutation-Based Minimal Test Suite Generation for Boolean Expressions
    Ayav, Tolga
    Belli, Fevzi
    INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, 2023, 33 (06) : 865 - 884