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Automatic debugging of operator errors based on efficient mutation analysis

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Abstract

It takes a lot of time and effort to manually locate and fix software bugs. This paper proposes a method for automatically debugging operator related bugs. Testing, fault localization, and bug-fixing are closely linked based on mutation analysis. However, in the process of mutation analysis, the generation of a large number of mutants and the execution of test cases on mutants, is fairly time-consuming. To solve this problem, optimization methods for selection of mutants and test cases have been proposed. Experiment results has shown that it can improve the efficiency of mutation analysis, so that the cost of fault-localization and bug-fixing can be reduced. We also implemented the exhaustive mutation method and the random mutation method and compared these three methods. These three method have different application scenarios. As the mutation based fault localization can rank statements by suspiciousness, the method integrated with fault localization is more stable and has batter performance. Also, it is more suitable for analyzing program with multi-bugs.

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References

  1. Debroy V, Wong WE (2014) Combining mutation and fault localization for automated program debugging. J Syst Softw 90(1):45–60

    Article  Google Scholar 

  2. Demillo RA, Lipton RJ, Sayward FG (1978) Hints on test data selection: help for the practicing programmer. Computer 11(4):34–41

    Article  Google Scholar 

  3. Gazzola L, Micucci D, Mariani L (2017) Automatic software repair: a survey. IEEE Trans Softw Eng PP(99):1–1

    Google Scholar 

  4. Gong P, Zhao R, Li Z (2015) Faster mutation-based fault localization with a novel mutation execution strategy. In: IEEE eighth international conference on software testing, verification and validation workshops, pp 1–10

  5. Huang JC (1978) Program instrumentation and software testing. Computer 11 (4):25–32

    Article  Google Scholar 

  6. Jia Y, Harman M (2011) An analysis and survey of the development of mutation testing. IEEE Trans Softw Eng 37(5):649–678

    Article  Google Scholar 

  7. Lin B, Guo W, Xiong N, Chen G, Vasilakos AV, Zhang H (2016) A pretreatment workflow scheduling approach for big data applications in multicloud environments. IEEE Trans Netw Serv Manag 13(3):581–594

    Article  Google Scholar 

  8. Moon S, Kim Y, Kim M, Yoo S (2014) Ask the mutants: mutating faulty programs for fault localization. In: IEEE seventh international conference on software testing, verification and validation, pp 153–162

  9. Offutt AJ (1996) An experimental determination of sufficient mutant operators. ACM Trans Softw Eng Methodol (TOSEM) 5(2):99–118

    Article  Google Scholar 

  10. Papadakis M, Traon YL (2014) Effective fault localization via mutation analysis: a selective mutation approach. In: ACM symposium on applied computing, pp 1293–1300

  11. Renieris M, Reiss SP (2003) Fault localization with nearest neighbor queries. In: Proceedings of IEEE international conference on automated software engineering, 2003, pp 30–39

  12. Rui A, Zoeteweij P, Gemund AJCV (2007) On the accuracy of spectrum-based fault localization. In: Testing: academic and industrial conference practice and research techniques - mutation, 2007. Taicpart-Mutation, pp 89–98

  13. Wei W, Yong Q (2011) Information potential fields navigation in wirelessad-hocsensor networks. Sensors 11(5):4794–4807

    Article  MathSciNet  Google Scholar 

  14. Wei W, Yang XL, Shen PY, Zhou B (2012) Holes detection in anisotropic sensornets: topological methods. Int J Distrib Sens Netw, 2012,(2012-10-23) 2012(2012):1–9

    Google Scholar 

  15. Wong WE, Gao R, Li Y, Rui A, Wotawa F (2016) A survey on software fault localization. IEEE Trans Softw Eng 42(8):707–740

    Article  Google Scholar 

  16. Zheng H, Guo W, Xiong N (2017) A kernel-based compressive sensing approach for mobile data gathering in wireless sensor network systems. IEEE Trans Syst Man Cybern Syst Hum PP(99):1–13

    Google Scholar 

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Acknowledgements

This study was supported by National Key R&D Program of China (Grant No. 2018YFB1004800), the National Natural Science Foundation of China(Grant No. 61672191) and Harbin science and technology innovation talents research project(Grant No. 2016RAQXJ013).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    TianTian Wang, JiaHuan Xu, XiaoHong Su, ChenShi Li & Yang Chi

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  1. TianTian Wang
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  2. JiaHuan Xu
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  3. XiaoHong Su
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  4. ChenShi Li
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  5. Yang Chi
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Correspondence to TianTian Wang.

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Wang, T., Xu, J., Su, X. et al. Automatic debugging of operator errors based on efficient mutation analysis. Multimed Tools Appl 78, 29881–29898 (2019). https://doi.org/10.1007/s11042-018-6603-3

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  • DOI: https://doi.org/10.1007/s11042-018-6603-3

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