Emerging topics in mining software repositories (original) (raw)

References

1. 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
   Article Google Scholar
2. 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
   Article Google Scholar
3. 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
   Article Google Scholar
4. 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)
   Google Scholar
5. 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
   Article Google Scholar
6. 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
   Article Google Scholar
7. 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
8. 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
   Article Google Scholar
9. 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
   Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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)
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
25. Jacobson, I., Booch, G., Rumbaugh, J.: The Unified Software Development Process. Addison-Wesley Longman Publishing Co., Inc., Boston, MA (1999)
    Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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)
    Article Google Scholar
33. Lehman, M.M., Belady, L.A. (eds.): Program Evolution: Processes of Software Change, 1st edn. Academic Press Professional Inc, San Diego, CA (1985)
    Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
38. Louridas, P., Ebert, C.: Machine learning. IEEE Softw. 33(5), 110–115 (2016). https://doi.org/10.1109/MS.2016.114
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
42. Porter, M.: An algorithm for suffix stripping. Program 3, 130–137 (1980)
    Article Google Scholar
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
    Article Google Scholar
44. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Burlington (1993)
    Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar
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
    Article Google Scholar

Download references