A novel approach for mutant diversity-based fault localization: DAM-FL

References

• Boehm BW. Software engineering economics. IEEE Trans Softw Eng. 1984;1:4–21. doi: https://doi.org/10.1109/TSE.1984.5010193
   Google Scholar
• Abreu R, Zoeteweij P, Golsteijn R, et al. A practical evaluation of spectrum-based fault localization. J Syst Softw. 2009;82(11): 1780–1792. doi: https://doi.org/10.1016/j.jss.2009.06.035
   Web of Science ®Google Scholar
• Jones JA, Harrold MJ. Empirical evaluation of the tarantula automatic fault-localization technique. Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering; 2005. p. 273–282.
   Google Scholar
• Naish L, Lee HJ, Ramamohana Rao K. A model for spectra-based software diagnosis. ACM Trans Softw Eng Methodol . 2011;20(3):11–48. doi: https://doi.org/10.1145/2000791.2000795
   Web of Science ®Google Scholar
• Wong WE, Debroy V, Gao R, et al. The DStar method for effective software fault localization. IEEE Trans Reliab. 2014;63(1):290–308. doi: https://doi.org/10.1109/TR.2013.2285319
   Web of Science ®Google Scholar
• Abreu R, Zoeteweij P, Van Gemund AJ. Spectrum-based multiple fault localization. Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering; 2009. p. 88–99.
   Google Scholar
• Papadakis M, Le Traon Y. Metallaxis-FL: mutation-based fault localization. Softw Test Verif Reliab. 2015;25(5–7):605–628. doi: https://doi.org/10.1002/stvr.1509
   Web of Science ®Google Scholar
• Moon S, Kim Y, Kim M, et al. Ask the mutants: mutating faulty programs for fault localization. 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation; 2014 Mar 31. p. 153–162.
   Google Scholar
• Debroy V, Wong WE. Using mutation to automatically suggest fixes for faulty programs. 2010 Third International Conference on Software Testing, Verification and Validation; 2010 Apr. p. 65–74.
   Google Scholar
• Hong S, Lee B, Kwak T, et al. Mutation-based fault localization for real-world multilingual programs (T). 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE); 2015 Nov 9. p. 464–475.
   Google Scholar
• Liu B, Nejati S, Briand LC. Improving fault localization for Simulink models using search-based testing and prediction models. 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER); 2017 Feb 20. p. 359–370.
   Google Scholar
• Xia X, Gong L, Le TD, et al. Diversity maximization speedup for localizing faults in single-fault and multi-fault programs. Autom Softw Eng. 2016;23(1):43–75. doi: https://doi.org/10.1007/s10515-014-0165-z
   Web of Science ®Google Scholar
• Ma YS, Kim SW. Mutation testing cost reduction by clustering overlapped mutants. J Syst Softw. 2016;115:18–30. doi: https://doi.org/10.1016/j.jss.2016.01.007
   Web of Science ®Google Scholar
• Wong WE, Delamaro ME, Maldonado JC, et al. Constrained mutation in C programs. Proceedings of the 8th Brazilian Symposium on Software Engineering 1994 Oct. . Curitiba, PR. Brazil: Brazilian Computer Society; 1994. p. 439–452.
   Google Scholar
• Offutt AJ, Lee A, Rothermel G, et al. An experimental determination of sufficient mutant operators. ACM Trans Softw Eng Methodol. 1996;5(2):99–118. doi: https://doi.org/10.1145/227607.227610
   Google Scholar
• Kaminski G, Ammann P, Offutt J. Improving logic-based testing. J Syst Softw. 2013;86(8):2002–2012. doi: https://doi.org/10.1016/j.jss.2012.08.024
   Web of Science ®Google Scholar
• Ammann P, Delamaro ME, Offutt J. Establishing theoretical minimal sets of mutants. 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation; 2014 Mar 31. p. 21–30.
   Google Scholar
• Shin D, Yoo S, Bae DH. Diversity-aware mutation adequacy criterion for improving fault detection capability. 2016 IEEE Ninth International Conference on Software Testing, Verification and Validation Workshops (ICSTW); 2016 Apr 11. p. 122–131.
   Google Scholar
• Shin D, Yoo S, Bae DH. A theoretical and empirical study of diversity-aware mutation adequacy criterion. IEEE Trans Softw Eng. 2018;44(10):914–931. doi: https://doi.org/10.1109/TSE.2017.2732347
   Web of Science ®Google Scholar
• Just R, Jalali D, Ernst MD. Defects4J: a database of existing faults to enable controlled testing studies for Java programs. Proceedings of the 2014 International Symposium on Software Testing and Analysis; 2014 Jul 21. p. 437–440.
   Google Scholar
• Hao D, Xie T, Zhang L, et al. Test input reduction for result inspection to facilitate fault localization. Autom Softw Eng. 2010;17(1):5. doi: https://doi.org/10.1007/s10515-009-0056-x
   Web of Science ®Google Scholar
• Vidács L, Beszédes Á, Tengeri D, et al. Test suite reduction for fault detection and localization: a combined approach. 2014 Software Evolution Week – IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE); 2014 Feb 3. p. 204–213.
   Google Scholar
• Geng J, Li Z, Zhao R, et al. Search based test suite minimization for fault detection and localization: a co-driven method. International Symposium on Search Based Software Engineering; 2016 Oct 8. p. 34–48.
   Google Scholar
• Jeffrey D, Gupta N, Gupta R. Fault localization using value replacement. Proceedings of the 2008 International Symposium on Software Testing and Analysis; 2008 Jul 20. p. 167–178.
   Google Scholar
• Just R. The Major mutation framework: efficient and scalable mutation analysis for Java. Proceedings of the 2014 International Symposium on Software Testing and Analysis; 2014 Jul 21. p. 433–436.
   Google Scholar
• Pearson S, Campos J, Just R, et al. Evaluating and improving fault localization. Proceedings of the 39th International Conference on Software Engineering; 2017 May 20. p. 609–620.
   Google Scholar
• Wong WE, Mathur AP. Fault detection effectiveness of mutation and data flow testing. Softw Qual J. 1995;4(1):69–83. doi: https://doi.org/10.1007/BF00404650
   Web of Science ®Google Scholar
• Just R, Jalali D, Inozemtseva L, et al. Are mutants a valid substitute for real faults in software testing? Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering; 2014 Nov 11. p. 654–665.
   Google Scholar
• Gupta N, Sharma A, Pachariya MK. An insight into test case optimization: ideas and trends with future perspectives. IEEE Access. 2019;7:22310–22327. doi: https://doi.org/10.1109/ACCESS.2019.2899471
   Web of Science ®Google Scholar