Download

Full Text

• S. Elbaum, G. Rothermel, and J. Penix, "Techniques for improving regression testing in continuous integration development environments, " in Proc. 22nd ACM SIGSOFT Int. Symp. Foundations Softw. Eng., 2014, pp. 235-245.
• H. Spieker, A. Gotlieb, D. Marijan, and M. Mossige, "Reinforcement learning for automatic test case prioritization and selection in continuous integration, " in Proc. 26th ACM SIGSOFT Int. Symp. Softw. Testing Anal., 2017, pp. 12-22.
• J. A. P. Lima, W. D. Mendonça, S. R. Vergilio, and W. K. Assunção, "Learning-based prioritization of test cases in continuous integration of highly-configurable software, " in Proc. 24th ACM Conf. Syst. Softw. Product Line: Volume A-Volume A, 2020, pp. 1-11.
• J. A. do Prado Lima and S. R. Vergilio, "A multi-Armed bandit approach for test case prioritization in continuous integration environments, " IEEE Trans. Softw. Eng., vol. 48, no. 2, pp. 453-465, Feb. 2022.
• A. Memon et al., "Taming Google-scale continuous testing, " in Proc. IEEE/ACM 39th Int. Conf. Softw. Eng.: Softw. Eng. Pract. Track, 2017, pp. 233-242.
• M. Bagherzadeh, N. Kahani, and L. Briand, "Reinforcement learning for test case prioritization, " IEEE Trans. Softw. Eng., no. 1, pp. 1-21, Apr., 2021.
• A. Gepperth and B. Hammer, "Incremental learning algorithms and applications, " in Proc. Eur. Symp. Artif. Neural Netw., 2016, pp. 1-12.
• R. Pan, M. Bagherzadeh, T. A. Ghaleb, and L. Briand, "Test case selection and prioritization using machine learning: A systematic literature review, " 2021, arXiv:2106.13891.
• A. Bertolino, A. Guerriero, B. Miranda, R. Pietrantuono, and S. Russo, "Learning-To-rank vs ranking-To-learn: Strategies for regression testing in continuous integration, " in Proc. ACM/IEEE 42nd Int. Conf. Softw. Eng., 2020, pp. 1-12.
• Mockus and Votta, "Identifying reasons for software changes using historic databases, " in Proc. Int. Conf. Softw. Maintenance, 2000, pp. 120-130.
• A.Hindle, D.M.German, M.W.Godfrey, and R.C.Holt, "Automatic classication of large changes into maintenance categories, " in Proc. IEEE 17th Int. Conf. Prog. Comprehension, 2009, pp. 30-39.
• S. Levin and A. Yehudai, "Boosting automatic commit classification into maintenance activities by utilizing source code changes, " in Proc. 13th Int. Conf. Predictive Models Data Analytics Softw. Eng., 2017, pp. 97-106. [Online]. Available: https://www.doi.org/10.1145/3127005.3127016
• S. Zafar, M. Z. Malik, and G. S. Walia, "Towards standardizing and improving classification of bug-fix commits, " in Proc. ACM/IEEE Int. Symp. Empir. Softw. Eng. Meas., 2019, pp. 1-6.
• J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pretraining of deep bidirectional transformers for language understanding, " in Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol., 2019, pp. 4171-4186. [Online]. Available: https://www.aclweb.org/anthology/N19-1423
• Y. Zhu et al., "Aligning books and movies: Towards story-like visual explanations by watching movies and reading books, " in Proc. IEEE Int. Conf. Comput. Vis., 2015, pp. 19-27.
• L. Breiman, "Random forests, " Mach. Learn., vol. 45, no. 1, pp. 5-32, Oct. 2001. [Online]. Available: https://doi.org/10.1023/A:10109 33404324
• C. Cortes and V. Vapnik, "Support-vector networks, " Mach. Learn., vol. 20, no. 3, pp. 273-297, Sep. 1995. [Online]. Available: https://doi.org/10.1007/BF00994018
• Wikipedia, "Tf-idf-Wikipedia, the free encyclopedia, " 2021. [Online]. Available: https://en.wikipedia.org/wiki/tf-idf
• E. D. Berger, C. Hollenbeck, P. Maj, O. Vitek, and J. Vitek, "On the impact of programming languages on code quality: A reproduction study, " ACM Trans. Program. Lang. Syst., vol. 41, no. 4, Oct. 2019, Art. no. 21. [Online]. Available: https://doi.org/10.1145/3340571
• T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system, " in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2016, pp. 785-794. [Online]. Available: https://doi.org/10.1145/2939672.2939785
• Scientific Toolworks, Inc., "UnderstandTM, " 2020, Accessed: Oct. 14, 2020. [Online]. Available: https://www.scitools.com/
• T. Mattis, P. Rein, F. Dürsch, and R. Hirschfeld, "RTPTorrent: An open-source dataset for evaluating regression test prioritization, " in Proc. 17th Int. Conf. Mining Softw. Repositories, 2020, pp. 385-396. [Online]. Available: https://doi.org/10.1145/3379597.3387458
• M. Beller, G. Gousios, and A. Zaidman, "TravisTorrent: Synthesizing travis CI and GitHub for full-stack research on continuous integration, " in Proc. IEEE/ACM 14th Work. Conf. Mining Softw. Repositories, 2017, pp. 447-450.
• B. Busjaeger and T. Xie, "Learning for test prioritization: An industrial case study, " in Proc. 24th ACM SIGSOFT Int. Symp. Foundations Softw. Eng., 2016, pp. 975-980.
• D. Elsner, F. Hauer, A. Pretschner, and S. Reimer, "Empirically evaluating readily available information for regression test optimization in continuous integration, " in Proc. 30th ACM SIGSOFT Int. Symp. Softw. Testing Anal., 2021, pp. 491-504.
• M. di Biase, A. Rastogi, M. Bruntink, and A. van Deursen, "The delta maintainability model: Measuring maintainability of fine-grained code changes, " in Proc. 2nd Int. Conf. Tech. Debt, 2019, pp. 113-122. [Online]. Available: https://doi.org/10.1109/TechDebt.2019.00030
• G. Gousios, "The ghtorrent dataset and tool suite, " in Proc. 10th Work. Conf. Mining Softw. Repositories, 2013, pp. 233-236. [Online]. Available: http://dl.acm.org/citation.cfm?id=2487085.2487132
• Wikipedia contributors, "68-95-99.7 rule-Wikipedia, the free encyclopedia, " 2022. [Online]. Available: https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7-rule
• S. Elbaum, A. Malishevsky, and G. Rothermel, "Incorporating varying test costs and fault severities into test case prioritization, " in Proc. 23rd Int. Conf. Softw. Eng., 2001, pp. 329-338.
• G. Rothermel, R. H. Untch, C. Chu, and M. J. Harrold, "Test case prioritization: An empirical study, " in Proc. IEEE Int. Conf. Softw. Maintenance, 1999, pp. 179-188.
• Q. Peng, A. Shi, and L. Zhang, "Empirically revisiting and enhancing IR-based test-case prioritization, " in Proc. 29th ACM SIGSOFT Int. Symp. Softw. Testing Anal., 2020, pp. 324-336. [Online]. Available: https://doi.org/10.1145/3395363.3397383
• J. Chen et al., "Optimizing test prioritization via test distribution analysis, " in Proc. 26th ACM Joint Meeting Eur. Softw. Eng. Conf. Symp. Foundations Softw. Eng., 2018, pp. 656-667.
• J. H. Friedman, "Greedy function approximation: A gradient boosting machine, " Ann. Statist., vol. 29, no. 5, pp. 1189-1232, 2001. [Online]. Available: https://doi.org/10.1214/aos/1013 203451
• Q. Wu, C. J. C. Burges, K. M. Svore, and J. Gao, "Adapting boosting for information retrieval measures, " Inf. Retrieval, vol. 13, no. 3, pp. 254-270, Jun. 2010. [Online]. Available: https://doi.org/10.1007/s10791-009-9112-1
• Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer, "An efficient boosting algorithm for combining preferences, " J. Mach. Learn. Res., vol. 4, no. Nov. pp. 933-969, 2003.
• Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li, "Learning to rank: From pairwise approach to listwise approach, " in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 129-136. [Online]. Available: https://doi.org/10.1145/1273496.1273513
• D. Metzler and W. Bruce Croft, "Linear feature-based models for information retrieval, " Inf. Retrieval, vol. 10, no. 3, pp. 257-274, Jun. 2007. [Online]. Available: https://doi.org/10.1007/s10791-006-9019-z
• Ranklib, 2020. [Online]. Available: https://sourceforge.net/p/lemur/wiki/RankLib/
• M. Friedman, "The use of ranks to avoid the assumption of normality implicit in the analysis of variance, " J. Amer. Statist. Assoc., vol. 32, no. 200, pp. 675-701, 1937.
• P. B. Nemenyi, "Distribution-free multiple comparisons, " Ph.D. dissertation, Princeton Univ., Princeton, NJ, USA, 1963. [Online]. Available: https://www.proquest.com/docview/302256074
• J. Torres-Jimenez and I. Izquierdo-Marquez, "Survey of covering arrays, " in Proc. 15th Int. Symp. Symbolic Numeric Algorithms Sci. Comput., 2013, pp. 20-27.
• M. Beller, G. Gousios, and A. Zaidman, "Oops, my tests broke the build: An explorative analysis of travis CI with GitHub, " in Proc. IEEE/ACM 14th Int. Conf.Mining Softw. Repositories, 2017, pp. 356-367.
• R. Somasundaram and R. Nedunchezhian, "Evaluation of three simple imputation methods for enhancing preprocessing of data with missing values, " Int. J. Comput. Appl., vol. 21, pp. 14-19, May 2011.
• D. Spadini, M. Aniche, and A. Bacchelli, "PyDriller: Python framework for mining software repositories, " in Proc. 26th ACM Joint Meeting Eur. Softw. Eng. Conf. Symp. Found. Softw. Eng., 2018, pp. 908-911.
• J.-M. Kim and A. Porter, "A history-based test prioritization technique for regression testing in resource constrained environments, " in Proc. 24th Int. Conf. Softw. Eng., 2002, pp. 119-129.
• G. Rothermel, R. H. Untch, C. Chu, and M. J. Harrold, "Prioritizing test cases for regression testing, " IEEE Trans. Softw. Eng., vol. 27, no. 10, pp. 929-948, Oct. 2001.
• J. A. Jones and M. J. Harrold, "Test-suite reduction and prioritization for modified condition/decision coverage, " IEEE Trans. Softw. Eng., vol. 29, no. 3, pp. 195-209, Mar. 2003.
• D. Jeffrey and N. Gupta, "Test case prioritization using relevant slices, " in Proc. 30th Annu. Int. Comput. Softw. Appl. Conf., 2006, pp. 411-420.
• M. Machalica, A. Samylkin, M. Porth, and S. Chandra, "Predictive test selection, " in Proc. IEEE/ACM 41st Int. Conf. Softw. Eng.: Softw. Eng. Pract., 2019, pp. 91-100.
• J. A. P. Lima and S. R. Vergilio, "Multi-Armed bandit test case prioritization in continuous integration environments: A trade-off analysis, " in Proc. 5th Braz. Symp. Systematic Automated Softw. Testing, 2020, pp. 21-30.
• L. Rosenbauer, A. Stein, R.Maier, D. Pätzel, and J. Hähner, "XCS as a reinforcement learning approach to automatic test case prioritization, " inProc. Genet. Evol. Comput. Conf. Companion, 2020, pp. 1798-1806.
• T. Shi, L. Xiao, and K. Wu, "Reinforcement learning based test case prioritization for enhancing the security of software, " in Proc. IEEE 7th Int. Conf. Data Sci. Adv. Analytics, 2020, pp. 663-672.
• R. Almaghairbe and M. Roper, "Separating passing and failing test executions by clustering anomalies, " Softw. Qual. J., vol. 25, no. 3, pp. 803-840, 2017.
• R. Carlson, H. Do, and A. Denton, "A clustering approach to improving test case prioritization: An industrial case study, " in Proc. 27th IEEE Int. Conf. Softw. Maintenance, 2011, pp. 382-391.
• S. Chen, Z. Chen, Z. Zhao, B. Xu, and Y. Feng, "Using semi-supervised clustering to improve regression test selection techniques, " in Proc. 4th IEEE Int. Conf. Softw. Testing, Verification Validation, 2011, pp. 1-10.
• P. Kandil, S. Moussa, and N. Badr, "Cluster-based test cases prioritization and selection technique for agile regression testing, " J. Softw.: Evol. Process, vol. 29, no. 6, 2017, Art. no. e1794.
• Z. Khalid and U. Qamar, "Weight and cluster based test case prioritization technique, " in Proc. IEEE 10th Annu. Inf. Technol., Electron. Mobile Commun. Conf., 2019, pp. 1013-1022.
• Y. Wang, Z. Chen, Y. Feng, B. Luo, and Y. Yang, "Using weighted attributes to improve cluster test selection, " in Proc. IEEE 6th Int. Conf. Softw. Secur. Rel., 2012, pp. 138-146.
• S. Yoo, M. Harman, P. Tonella, and A. Susi, "Clustering test cases to achieve effective and scalable prioritisation incorporating expert knowledge, " in Proc. 18th Int. Symp. Softw. Testing Anal., 2009, pp. 201-212.
• M. Hasnain, M. F. Pasha, C. H. Lim, and I. Ghan, "Recurrent neural network for web services performance forecasting, ranking and regression testing, " in Proc. Asia-Pacific Signal Inf. Process. Assoc. Annu. Summit Conf., 2019, pp. 96-105.
• H. Jahan, Z. Feng, S. Mahmud, and P. Dong, "Version specific test case prioritization approach based on artificial neural network, " J. Intell. Fuzzy Syst., vol. 36, no. 6, pp. 6181-6194, 2019.
• R. Lachmann, S. Schulze, M. Nieke, C. Seidl, and I. Schaefer, "System-level test case prioritization using machine learning, " in Proc. 15th IEEE Int. Conf. Mach. Learn. Appl., 2016, pp. 361-368.
• M. Mahdieh, S.-H. Mirian-Hosseinabadi, K. Etemadi, A. Nosrati, and S. Jalali, "Incorporating fault-proneness estimations into coverage-based test case prioritization methods, " Inf. Softw. Technol., vol. 121, 2020, Art. no. 106269.
• S. Mirarab and L. Tahvildari, "An empirical study on Bayesian network-based approach for test case prioritization, " in Proc. 1st Int. Conf. Softw. Testing, Verification, Validation, 2008, pp. 278-287.
• T. B. Noor and H. Hemmati, "Studying test case failure prediction for test case prioritization, " in Proc. 13th Int. Conf. Predictive Models Data Analytics Softw. Eng., 2017, pp. 2-11.
• F. Palma, T. Abdou, A. Bener, J. Maidens, and S. Liu, "An improvement to test case failure prediction in the context of test case prioritization, " in Proc. 14th Int. Conf. Predictive Models Data Analytics Softw. Eng., 2018, pp. 80-89.
• M. M. Sharma and A. Agrawal, "Test case design and test case prioritization using machine learning, " Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 2742-2748, 2019.
• A. Singh, R. K. Bhatia, and A. Singhrova, "Machine learning based test case prioritization in object oriented testing, " Int. J. Recent Technol. Eng., vol. 8, no. 3, pp. 700-707, 2019.
• P. Tonella, P. Avesani, and A. Susi, "Using the case-based ranking methodology for test case prioritization, " in Proc. 22nd IEEE Int. Conf. Softw. Maintenance, 2006, pp. 123-133.
• H. Aman, S. Amasaki, T. Yokogawa, and M. Kawahara, "A comparative study of vectorization-based static test case prioritization methods, " in Proc. 46th Euromicro Conf. Softw. Eng. Adv. Appl., 2020, pp. 80-88.
• S. W. Thomas, H. Hemmati, A. E. Hassan, and D. Blostein, "Static test case prioritization using topic models, " Empir. Softw. Eng., vol. 19, no. 1, pp. 182-212, 2014.