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Progress in Artificial Intelligence

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28-03-2018 | Regular Paper

Emerging topics in mining software repositories

Machine learning in software repositories and datasets

Authors: Diego Güemes-Peña, Carlos López-Nozal, Raúl Marticorena-Sánchez, Jesús Maudes-Raedo

Published in: Progress in Artificial Intelligence | Issue 3/2018

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Abstract

A software process is a set of related activities that culminates in the production of a software package: specification, design, implementation, testing, evolution into new versions, and maintenance. There are also other supporting activities such as configuration and change management, quality assurance, project management, evaluation of user experience, etc. Software repositories are infrastructures to support all these activities. They can be composed with several systems that include code change management, bug tracking, code review, build system, release binaries, wikis, forums, etc. This position paper on mining software repositories presents a review and a discussion of research in this field over the past decade. We also identify applied machine learning strategies, current working topics, and future challenges for the improvement of company decision-making systems. Machine learning is defined as the process of discovering patterns in data. It can be applied to software repositories, since every change is recorded as data. Companies can then use these patterns as the basis for their decision-making systems and for knowledge discovery.

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https://sourceforge.net/.

https://github.com/.

https://about.gitlab.com/.

https://bitbucket.org/.

https://travis-ci.org/.

https://www.codacy.com/.

http://www.msrconf.org/.

http://text-processing.com/demo/sentiment/.

Ali, N., Guhneuc, Y.G., Antoniol, G.: Trustrace: mining software repositories to improve the accuracy of requirement traceability links. IEEE Trans. Softw. Eng. 39(5), 725–741 (2013). https://doi.org/10.1109/TSE.2012.71 CrossRef

Arnaoudova, V., Eshkevari, L., Penta, M., Oliveto, R., Antoniol, G., Guhneuc, Y.G.: Repent: analyzing the nature of identifier renamings. IEEE Trans. Softw. Eng. 40(5), 502–532 (2014). https://doi.org/10.1109/TSE.2014.2312942 CrossRef

Bavota, G., Linares-Vsquez, M., Bernal-Crdenas, C., Di Penta, M., Oliveto, R., Poshyvanyk, D.: The impact of api change- and fault-proneness on the user ratings of android apps. IEEE Trans. Softw. Eng. 41(4), 384–407 (2015). https://doi.org/10.1109/TSE.2014.2367027 CrossRef

Brown, W.H., Malveau, R.C., McCormick, H.W.S., Mowbray, T.J.: AntiPatterns: Refactoring Software, Architectures, and Projects in Crisis, 1st edn. Wiley, New York, NY (1998)

Canfora, G., Cerulo, L., Cimitile, M., Di Penta, M.: How changes affect software entropy: an empirical study. Empir. Softw. Eng. 19(1), 1–38 (2014). https://doi.org/10.1007/s10664-012-9214-z CrossRef

Chen, T.H., Thomas, S., Hassan, A.: A survey on the use of topic models when mining software repositories. Empir. Softw. Eng. 21(5), 1843–1919 (2016). https://doi.org/10.1007/s10664-015-9402-8 CrossRef

Chowdhury, S.A., Hindle, A.: Mining stackoverflow to filter out off-topic irc discussion. In: Proceedings of the 12th Working Conference on Mining Software Repositories, MSR ’15, pp. 422–425. IEEE Press, Piscataway, NJ, USA (2015). http://dl.acm.org/citation.cfm?id=2820518.2820577

Dagenais, B., Robillard, M.: Recommending adaptive changes for framework evolution. ACM Trans. Softw. Eng. Methodol. 20(4), 9 (2011). https://doi.org/10.1145/2000799.2000805 CrossRef

Destefanis, G., Ortu, M., Counsell, S., Swift, S., Marchesi, M., Tonelli, R.: Software development: do good manners matter? PeerJ Comput. Sci. (2016). https://doi.org/10.7717/peerj-cs.73 CrossRef

10.

Dyer, R., Nguyen, H., Rajan, H., Nguyen, T.: Boa: Ultra-large-scale software repository and source-code mining. ACM Trans. Softw. Eng. Methodol. 25(1), 7 (2015). https://doi.org/10.1145/2803171 CrossRef

11.

German, D.M.: A study of the contributors of postgresql. In: Proceedings of the 2006 International Workshop on Mining Software Repositories, MSR ’06, pp. 163–164. ACM, New York, NY, USA (2006). https://doi.org/10.1145/1137983.1138022

12.

Gonzalez-Barahona, J., Robles, G., Herraiz, I., Ortega, F.: Studying the laws of software evolution in a long-lived floss project. J. Softw. Evolut. Process 26(7), 589–612 (2014). https://doi.org/10.1002/smr.1615 CrossRef

13.

Gonzlez-Barahona, J., Robles, G.: On the reproducibility of empirical software engineering studies based on data retrieved from development repositories. Empir. Softw. Eng. 17(1–2), 75–89 (2012). https://doi.org/10.1007/s10664-011-9181-9 CrossRef

14.

Goyal, A., Sardana, N.: Nrfixer: Sentiment based model for predicting the fixability of non-reproducible bugs. E-Inf. Softw. Eng. J. 11(1), 103–116 (2017). https://doi.org/10.5277/e-Inf170105 CrossRef

15.

Grant, S., Betts, B.: Encouraging user behaviour with achievements: an empirical study. In: Proceedings of the 10th Working Conference on Mining Software Repositories, MSR ’13, pp. 65–68. IEEE Press, Piscataway, NJ, USA (2013). http://dl.acm.org/citation.cfm?id=2487085.2487101

16.

Guana, V., Rocha, F., Hindle, A., Stroulia, E.: Do the stars align? Multidimensional analysis of android’s layered architecture. In: 2012 9th IEEE Working Conference on Mining Software Repositories (MSR), pp. 124–127 (2012)

17.

Guzman, E., Azócar, D., Li, Y.: Sentiment analysis of commit comments in GitHub: an empirical study. In: Proceedings of the 11th Working Conference on Mining Software Repositories, MSR 2014, pp. 352–355. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2597073.2597118

18.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)CrossRef

19.

Hammad, M., Hammad, M., Bani-Salameh, H.: Identifying designers and their design knowledge. Int. J. Softw. Eng. Its Appl. 7(6), 277–288 (2013). https://doi.org/10.14257/ijseia.2013.7.6.23 CrossRef

20.

Han, J., Jung, W.: Extracting communication structure of a development organization from a software repository. Pers. Ubiquit. Comput. 18(6), 1413–1421 (2014). https://doi.org/10.1007/s00779-013-0742-3 CrossRef

21.

Hassan, A., Holt, R.: Replaying development history to assess the effectiveness of change propagation tools. Empir. Softw. Eng. 11(3), 335–367 (2006). https://doi.org/10.1007/s10664-006-9006-4 CrossRef

22.

Hindle, A.: Green mining: a methodology of relating software change and configuration to power consumption. Empir. Softw. Eng. 20(2), 374–409 (2015). https://doi.org/10.1007/s10664-013-9276-6 CrossRef

23.

Holmes, R., Walker, R.J.: A newbie’s guide to eclipse APIs. In: Proceedings of the 2008 International Working Conference on Mining Software Repositories, MSR 2008 (Co-located with ICSE), Leipzig, Germany, May 10–11, 2008, Proceedings, pp. 149–152 (2008). https://doi.org/10.1145/1370750.1370787

24.

Hora, A., Anquetil, N., Etien, A., Ducasse, S., Valente, M.: Automatic detection of system-specific conventions unknown to developers. J. Syst. Softw. 109, 192–204 (2015). https://doi.org/10.1016/j.jss.2015.08.007 CrossRef

25.

Jacobson, I., Booch, G., Rumbaugh, J.: The Unified Software Development Process. Addison-Wesley Longman Publishing Co., Inc., Boston, MA (1999)

26.

Kagdi, H., Gethers, M., Poshyvanyk, D., Hammad, M.: Assigning change requests to software developers. J. Softw. Evolut. Process 24(1), 3–33 (2012). https://doi.org/10.1002/smr.530 CrossRef

27.

Kamei, Y., Shihab, E., Adams, B., Hassan, A., Mockus, A., Sinha, A., Ubayashi, N.: A large-scale empirical study of just-in-time quality assurance. IEEE Trans. Softw. Eng. 39(6), 757–773 (2013). https://doi.org/10.1109/TSE.2012.70 CrossRef

28.

Khomh, F., Penta, M., Guhneuc, Y.G., Antoniol, G.: An exploratory study of the impact of antipatterns on class change- and fault-proneness. Empir. Softw. Eng. 17(3), 243–275 (2012). https://doi.org/10.1007/s10664-011-9171-y CrossRef

29.

Kim, S., Shivaji, S., Whitehead Jr., E.: Kenyon-web: Reconfigurable web-based feature extractor. Vancouver, BC, pp. 287–288 (2009). https://doi.org/10.1109/ICPC.2009.5090061

30.

Kirbas, S., Caglayan, B., Hall, T., Counsell, S., Bowes, D., Sen, A., Bener, A.: The relationship between evolutionary coupling and defects in large industrial software. J. Softw. Evolut. Process (2017). https://doi.org/10.1002/smr.1842 CrossRef

31.

Krinke, J., Gold, N., Jia, Y., Binkley, D.: Cloning and copying between gnome projects. In: 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), pp. 98–101 (2010)

32.

Kumaresh, S., Baskaran, R.: Mining software repositories for defect categorization. J. Commun. Softw. Syst. 11(1), 31–36 (2015)CrossRef

33.

Lehman, M.M., Belady, L.A. (eds.): Program Evolution: Processes of Software Change, 1st edn. Academic Press Professional Inc, San Diego, CA (1985)

34.

Li, H., Shang, W., Zou, Y., Hassan, A.E.: Towards just-in-time suggestions for log changes. Empir. Softw. Eng. 22(4), 1831–1865 (2017). https://doi.org/10.1007/s10664-016-9467-z CrossRef

35.

Linares-Vásquez, M., Vendome, C., Tufano, M., Poshyvanyk, D.: How developers micro-optimize android apps. J. Syst. Softw. 130, 1–23 (2017). https://doi.org/10.1016/j.jss.2017.04.018 CrossRef

36.

Linstead, E., Rigor, P., Bajracharya, S., Lopes, C., Baldi, P.: Mining eclipse developer contributions via author-topic models. In: Fourth International Workshop on Mining Software Repositories (MSR’07:ICSE Workshops 2007), pp. 30–30 (2007)

37.

López-Fernández, L., Robles, G., Gonzalez-Barahona, J., Herraiz, I.: Applying social network analysis techniques to community-driven libre software projects. Int. J. Inf. Technol. Web Eng. (IJITWE) 1(3), 27–48 (2006). https://doi.org/10.4018/jitwe.2006070103 CrossRef

38.

Louridas, P., Ebert, C.: Machine learning. IEEE Softw. 33(5), 110–115 (2016). https://doi.org/10.1109/MS.2016.114 CrossRef

39.

Munaiah, N., Camilo, F., Wigham, W., Meneely, A., Nagappan, M.: Do bugs foreshadow vulnerabilities? An in-depth study of the chromium project. Empir. Softw. Eng. 22(3), 1305–1347 (2017). https://doi.org/10.1007/s10664-016-9447-3 CrossRef

40.

Munaiah, N., Kroh, S., Cabrey, C., Nagappan, M.: Curating github for engineered software projects. Empir. Softw. Eng. 22(6), 3219–3253 (2017). https://doi.org/10.1007/s10664-017-9512-6 CrossRef

41.

Penta, M., Cerulo, L., Aversano, L.: The life and death of statically detected vulnerabilities: an empirical study. Inf. Softw. Technol. 51(10), 1469–1484 (2009). https://doi.org/10.1016/j.infsof.2009.04.013 CrossRef

42.

Porter, M.: An algorithm for suffix stripping. Program 3, 130–137 (1980)CrossRef

43.

Prechelt, L., Pepper, A.: Why software repositories are not used for defect-insertion circumstance analysis more often: a case study. Inf. Softw. Technol. 56(10), 1377–1389 (2014). https://doi.org/10.1016/j.infsof.2014.05.001 CrossRef

44.

Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Burlington (1993)

45.

Rebouças, M., Santos, R.O., Pinto, G., Castor, F.: How does contributors’ involvement influence the build status of an open-source software project? In: Proceedings of the 14th International Conference on Mining Software Repositories, MSR ’17, pp. 475–478. IEEE Press, Piscataway, NJ, USA (2017). https://doi.org/10.1109/MSR.2017.32

46.

Robles, G., Gonzalez-Barahona, J.: Contributor turnover in libre software projects. IFIP Int. Fed. Inf. Process. 203, 273–286 (2006). https://doi.org/10.1007/0-387-34226-5_28 CrossRef

47.

Santos, E.A., Hindle, A.: Judging a commit by its cover: Correlating commit message entropy with build status on travis-ci. In: Proceedings of the 13th International Conference on Mining Software Repositories, MSR ’16, pp. 504–507. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2901739.2903493

48.

Schröter, A.: Msr challenge 2011: Eclipse, netbeans, firefox, and chrome. In: Proceedings of the 8th Working Conference on Mining Software Repositories, MSR ’11, pp. 227–229. ACM, New York, NY, USA (2011). https://doi.org/10.1145/1985441.1985478

49.

Shihab, E., Jiang, Z.M., Hassan, A.E.: On the use of internet relay chat (IRC) meetings by developers of the gnome gtk+ project. In: 2009 6th IEEE International Working Conference on Mining Software Repositories, pp. 107–110 (2009)

50.

Sun, X., Li, B., Duan, Y., Shi, W., Liu, X.: Mining software repositories for automatic interface recommendation. Sci. Program. (2016). https://doi.org/10.1155/2016/5475964 CrossRef

51.

Sun, X., Li, B., Leung, H., Li, B., Li, Y.: Msr4sm: using topic models to effectively mining software repositories for software maintenance tasks. Inf. Softw. Technol. 66, 1–12 (2015). https://doi.org/10.1016/j.infsof.2015.05.003 CrossRef

52.

Sun, Y., Wang, Q., Yang, Y.: Frlink: improving the recovery of missing issue-commit links by revisiting file relevance. Inf. Softw. Technol. 84, 33–47 (2017). https://doi.org/10.1016/j.infsof.2016.11.010 CrossRef

53.

Tappolet, J., Kiefer, C., Bernstein, A.: Semantic web enabled software analysis. J. Web Semant. 8(2–3), 225–240 (2010). https://doi.org/10.1016/j.websem.2010.04.009 CrossRef

54.

Teixeira, J., Robles, G., Gonzlez-Barahona, J.: Lessons learned from applying social network analysis on an industrial free/libre/open source software ecosystem. J. Internet Serv. Appl. 6(1), 14 (2015). https://doi.org/10.1186/s13174-015-0028-2 CrossRef

55.

Thummalapenta, S., Cerulo, L., Aversano, L., Di Penta, M.: An empirical study on the maintenance of source code clones. Empir. Softw. Eng. 15(1), 1–34 (2010). https://doi.org/10.1007/s10664-009-9108-x CrossRef

56.

Vanya, A., Klusener, S., Premraj, R., Van Vliet, H.: Supporting software architects to improve their software system’s decomposition—lessons learned. J. Softw. Evolut. Process 25(3), 219–232 (2013). https://doi.org/10.1002/smr.574 CrossRef

57.

Vendome, C., Bavota, G., Penta, M., Linares-Vsquez, M., German, D., Poshyvanyk, D.: License usage and changes: a large-scale study on github. Empir. Softw. Eng. 22(3), 1537–1577 (2017). https://doi.org/10.1007/s10664-016-9438-4 CrossRef

58.

Voinea, L., Telea, A.: Visual querying and analysis of large software repositories. Empir. Softw. Eng. 14(3), 316–340 (2009). https://doi.org/10.1007/s10664-008-9068-6 CrossRef

59.

Xuan, J., Jiang, H., Hu, Y., Ren, Z., Zou, W., Luo, Z., Wu, X.: Towards effective bug triage with software data reduction techniques. IEEE Trans. Knowl. Data Eng. 27(1), 264–280 (2015). https://doi.org/10.1109/TKDE.2014.2324590 CrossRef

60.

Yamashita, K., Kamei, Y., McIntosh, S., Hassan, A., Ubayashi, N.: Magnet or sticky? Measuring project characteristics from the perspective of developer attraction and retention. J. Inf. Process. 24(2), 339–348 (2016). https://doi.org/10.2197/ipsjjip.24.339 CrossRef

61.

Yuan, Z., Yu, L.L., Liu, C.: Bug prediction method for fine-grained source code changes. Ruan Jian Xue Bao/J. Softw. 25(11), 2499–2517 (2014). https://doi.org/10.13328/j.cnki.jos.004559 CrossRef

62.

Zamani, S., Lee, S., Shokripour, R., Anvik, J.: A feature location approach supported by time-aware weighting of terms associated with developer expertise profiles. Knowl. Inf. Syst. 49(2), 629–659 (2016). https://doi.org/10.1007/s10115-015-0909-5 CrossRef

63.

Zhou, M., Mockus, A.: Who will stay in the floss community? Modeling participant’s initial behavior. IEEE Trans. Software Eng. 41(1), 82–99 (2015). https://doi.org/10.1109/TSE.2014.2349496 CrossRef

Title: Emerging topics in mining software repositories
Machine learning in software repositories and datasets
Authors: Diego Güemes-Peña
Carlos López-Nozal
Raúl Marticorena-Sánchez
Jesús Maudes-Raedo
Publication date: 28-03-2018
Publisher: Springer Berlin Heidelberg
Published in: Progress in Artificial Intelligence / Issue 3/2018
Print ISSN: 2192-6352
Electronic ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-018-0147-7

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