Bug Localization on Single-Line Bugs of Apache Commons Math Library

Authors: Cherry Oo, Hnin Min Oo

Abstract:

Software bug localization is one of the most costly tasks in program repair technique. Therefore, there is a high claim for automated bug localization techniques that can monitor programmers to the locations of bugs, with slight human arbitration. Spectrum-based bug localization aims to help software developers to discover bugs rapidly by investigating abstractions of the program traces to make a ranking list of most possible buggy modules. Using the Apache Commons Math library project, we study the diagnostic accuracy using our spectrum-based bug localization metric. Our outcomes show that the greater performance of a specific similarity coefficient, used to inspect the program spectra, is mostly effective on localizing of single line bugs.

Keywords: Software testing, fault localization, program spectra.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2571977

References:

[1] Abreu, R., Zoeteweij, P., Golsteijn, R. and Van Gemund, A.J., 2009. A practical evaluation of spectrum-based fault localization. Journal of Systems and Software, 82(11), pp.1780-1792.
[2] Abreu, R., Zoeteweij, P. and Van Gemund, A.J., 2006, December. An evaluation of similarity coefficients for software fault localization. In Dependable Computing, 2006. PRDC'06. 12th Pacific Rim International Symposium on (pp. 39-46). IEEE.
[3] Abreu, R., Zoeteweij, P. and Van Gemund, A.J., 2007, September. On the accuracy of spectrum-based fault localization. In Testing: Academic and Industrial Conference Practice and Research Techniques-Mutation (Taicpart-Mutation 2007) (pp. 89-98). IEEE.
[4] Abreu, R., Zoeteweij, P. and Van Gemund, A.J., 2009, November. Spectrum-based multiple fault localization. In Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering (pp. 88-99). IEEE Computer Society.
[5] Chen, M.Y., Kiciman, E., Fratkin, E., Fox, A. and Brewer, E., 2002, June. Pinpoint: Problem determination in large, dynamic internet services. In null (p. 595). IEEE.
[6] Fu, W., Yu, H., Fan, G., Ji, X. and Pei, X., 2017, November. A Test Suite Reduction Approach to Improving the Effectiveness of Fault Localization. In Software Analysis, Testing and Evolution (SATE), 2017 International Conference on (pp. 10-19). IEEE.
[7] Gharibi, R., Rasekh, A.H. and Sadreddini, M.H., 2017, October. Locating relevant source files for bug reports using textual analysis. In Computer Science and Software Engineering Conference (CSSE), 2017 International Symposium on (pp. 67-72). IEEE.
[8] Hall, T., Zhang, M., Bowes, D. and Sun, Y., 2014. Some code smells have a significant but small effect on faults. ACM Transactions on Software Engineering and Methodology (TOSEM), 23(4), p.33.
[9] Jones, J.A. and Harrold, M.J., 2005, November. Empirical evaluation of the tarantula automatic fault-localization technique. In Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering (pp. 273-282). ACM.
[10] Laghari, G., Murgia, A. and Demeyer, S., 2016, August. Fine-tuning spectrum based fault localisation with frequent method item sets. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering (pp. 274-285). ACM.
[11] Le, T. D. B., Lo, D. and Li, M., 2015, September. Constrained feature selection for localizing faults. In 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME) (pp. 501-505). IEEE.
[12] Le, T. D. B., Oentaryo, R. J. and Lo, D., 2015, August. Information retrieval and spectrum based bug localization: better together. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (pp. 579-590). ACM.
[13] Pearson, S., Campos, J., Just, R., Fraser, G., Abreu, R., Ernst, M.D., Pang, D. and Keller, B., 2017, May. Evaluating and improving fault localization. In Proceedings of the 39th International Conference on Software Engineering (pp. 609-620). IEEE Press.
[14] Schneidewind, N., Montrose, M., Feinberg, A., Ghazarian, A., McLinn, J., Hansen, C., Laplante, P., Sinnadurai, N., Zio, E., Linger, R. and Wong, E., 2010. IEEE Reliability Society Technical Operations Annual Technical Report for 2010. IEEE Transactions on Reliability, 59(3), pp.449-482.
[15] Wong, W.E., Qi, Y., Zhao, L. and Cai, K.Y., 2007, July. Effective fault localization using code coverage. In Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International (Vol. 1, pp. 449-456). IEEE.
[16] Xie, X., Chen, T.Y., Kuo, F.C. and Xu, B., 2013. A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization. ACM Transactions on Software Engineering and Methodology (TOSEM), 22(4), p.31.
[17] Xu, Y., Yin, B., Zheng, Z., Zhang, X., Li, C. and Yang, S., 2019. Robustness of spectrum-based fault localisation in environments with labelling perturbations. Journal of Systems and Software, 147, pp.172-214.
[18] Youm, K.C., Ahn, J. and Lee, E., 2017. Improved bug localization based on code change histories and bug reports. Information and Software Technology, 82, pp.177-192.
[19] Zhang, M., Li, X., Zhang, L. and Khurshid, S., 2017, July. Boosting spectrum-based fault localization using PageRank. In Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 261-272). ACM.
[20] JUnit, http://www.junit.org.