Predicting higher order mutation score based on machine learning

References

• Microsoft (2023).
   Google Scholar
• Acree, A. T., Budd, T. A., DeMillo, R. A., Lipton, R. J., & Sayward, F. G. (1979). Mutation analysis (Technique Report GIT-ICS-79/08). Georgia Institute of Technology.
   Google Scholar
• Alireza, A., & Mirian-Hosseinabadi, S. H. (2020). The threat to the validity of predictive mutation testing: The impact of uncovered mutants, ArXiv abs/2005.11532.
   Google Scholar
• Berkson, J. (1944, September). Application of the logistic function to bio-assay. Journal of the American Statistical Association, 39(227), 357–365.
   Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Chekam, T. T., Papadakis, M., Bissyandé, T., & Le Traon, Y. (2018). Predicting the fault revelation utility of mutants. In 2018 IEEE/ACM 40th international conference on software engineering: Companion (ICSE-Companion) (pp. 408–409).
   Google Scholar
• DeMillo, R. A., Lipton, R. J., & Sayward, F. G. (1978). Hints on test data selection: lp for the practicing programmer. IEEE Comput, 11(4), 34–41. https://doi.org/10.1109/C-M.1978.218136
   Google Scholar
• Derezinska, A., & Halas, K. (2014). Experimental evaluation of mutation testing approaches to python programs. In 2014 IEEE seventh international conference on software testing, verification and validation workshops (pp. 156–164).
   Google Scholar
• Hamlet, R. G. (1977). Testing programs with the aid of a compiler. IEEE Transactions on Software Engineering, SE-3(4), 279–290. https://doi.org/10.1109/TSE.1977.231145
   Web of Science ®Google Scholar
• Jia, J., & Harman, M. (2008). Constructing subtle faults using higher order mutation testing. In Proc. eighth int'l working conf. source code analysis and manipulation (pp. 249–258).
   Google Scholar
• Jia, Y., & Harman, H. (2011). An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering, 37, 649–678. https://doi.org/10.1109/TSE.2010.62
   Web of Science ®Google Scholar
• Kim, J., Jeon, J., Hong, S., & Yoo, S. (2022). Predictive mutation analysis via the natural language channel in source code. ACM Transactions on Software Engineering and Methodology, 31(4), 1–27. https://doi.org/10.1145/3510417
   Web of Science ®Google Scholar
• Madeyski, L. (2007). On the effects of pair programming on thoroughness and fault-finding effectiveness of unit tests. In J. Münch & P. Abrahamsson (Eds.), Product-Focused Software Process Improvement (pp. 207–221). 4589 of Lecture notes in computer science Springer.
   Google Scholar
• Strug, J., & Strug, B. (2012). Machine learning approach in mutation testing. In: B. Nielsen, C. Weise (Eds.), Testing software and systems, ICTSS 2012 (pp. 200–214). Lecture notes in computer science: Vol. 7641 Springer.
   Google Scholar
• Tan, L., Gong, Y., & Wang, Y. (2019). A model for predicting statement mutation scores. Mathematics, 7(9), 778. https://doi.org/10.3390/math7090778
   Web of Science ®Google Scholar
• The XGBoost Contributors (2023). XGBoost Documentation [Computer software]. XGBoost.
   Google Scholar
• Van-Nho, D., Vu, N. Q., & Binh, N. T. (2019). A solution for improving the effectiveness of higher order mutation testing. In 2019 IEEE-RIVF international conference on computing and communication technologies (RIVF) (pp. 1–5).
   Google Scholar
• Van-Nho, D., Vu, N. Q., & Binh, N. T. (2021). Evaluating mutation operator and test case effectiveness by means of mutation testing. In N.T. Nguyen, S. Chittayasothorn, D. Niyato, B. Trawiński (Eds.), Intelligent information and database systems, ACIIDS 2021 (pp. 837–850) Lecture notes in computer science: Vol. 12672 Springer.
   Google Scholar
• Van-Nho, D., Vu, N. Q., & Binh, N. T. (2022, May). Toward improving the quality of mutation operator and test case effectiveness in higher-order mutation testing. Vietnam-Journal-of-Computer-Science, 2196–8888.
   Google Scholar
• Vu, N. Q., & Madeyski, L. (2014). Problems of mutation testing and higher order mutation testing. In Advanced computational methods for knowledge engineering (pp. 157–172). Advances in intelligent systems and computing: Vol. 282.
   Google Scholar
• Vu, N. Q., & Madeyski, L. (2015). Searching for strongly subsuming higher order mutants by applying multiobjective optimization algorithm. Advanced computational methods for knowledge engineering (pp. 391–402). Advances in intelligent systems and computing: Vol. 358.
   Google Scholar
• Vu, N. Q., & Madeyski, L. (2016a). Higher order mutation testing to drive development of new test cases: An empirical comparison of three strategies. In Lecture notes in computer science (Vol. 9621, pp. 235–244).
   Google Scholar
• Vu, N. Q., & Madeyski, L. (2016b). On the relationship between the order of mutation testing and the properties of generated higher order mutants. In Lecture notes in computer science (pp. 245–254).
   Google Scholar
• Vu, N. Q., & Madeyski, L. (2016c). Empirical evaluation of multi-objective optimization algorithms searching for higher order mutants. Cybernetic and Systems: An International Journal, 47(1–2), 48–68.
   Web of Science ®Google Scholar
• Vu, N. Q., & Madeyski, L. (2017). Addressing mutation testing problems by applying multi-objective optimization algorithms and higher order mutation. Journal of Intelligent & Fuzzy Systems, 32(2), 1173–1182. https://doi.org/10.3233/JIFS-169117
   Web of Science ®Google Scholar
• Zhang, J., Zhang, L., Harman, M., Hao, D., Jia, Y., & Zhang, L. (2019). Predictive mutation testing. IEEE Transactions on Software Engineering, 45(9), 898–918. https://doi.org/10.1109/TSE.32
   Web of Science ®Google Scholar