Source code management systems (such as git) record changes to code repositories of Open-Source Software (OSS) projects. The metadata about a change includes a change message to record the intention of the change. Classification of changes,based on change messages, into different change types has been explored in the past to understand the evolution of software systems from the perspective of change size and change density only. However, software evolution analysis based on change classification with a focus on change evolution patterns is still an open research problem. This study examines change messages of 106 OSS projects, as recorded in the git repository, to explore their evolutionary patterns with respect to the types of changes performed over time. An automated keyword-based classifier technique is applied to the change messages to categorize the changes into various types (corrective, adaptive, perfective, preventive, and enhancement). Cluster analysis helps to uncover distinct change patterns that each change type follows. We identify three categories of 106 projects for each change type: high activity, moderate activity, and low activity. Evolutionary behavior is different for projects of different categories. The projects with high and moderate activity receive maximum changes during 76–81 months of the project lifetime. The project attributes such as the number of committers, number of files changed, and total number of commits seem to contribute the most to the change activity of the projects. The statistical findings show that the change activity of a project is related to the number of contributors, amount of work done, and total commits of the projects irrespective of the change type. Further, we explored languages and domains of projects to correlate change types with domains and languages of the projects. The statistical analysis indicates that there is no significant and strong relation of change types with domains and languages of the 106 projects.
Hindle A, Godfrey M, Holt R C. Mining recurrent activities: fourier analysis of change events. In: Proceedings of the 31st International Conference on Software Engineering-Companion. 2009, 295–298 https://doi.org/10.1109/ICSE-COMPANION.2009.5071005
3
Mockus A, Votta L G. Identifying reasons for software changes using historic databases. In: Proceedings of International Conference on Software Maintenance. 2000, 120–130 https://doi.org/10.1109/ICSM.2000.883028
4
Hassan A. Automated classification of change messages in open source projects. ACM Symposium on Applied Computing. 2008, 837–841 https://doi.org/10.1145/1363686.1363876
5
Kolassa C, Riehle D, Salim M. The empirical commit frequency distribution of open source projects. In: Proceedings of ACM Joint International Symposium on Wikis and Open Collaboration. 2013 https://doi.org/10.1145/2491055.2491073
6
Lin S H, Ma Y T, Chen J X. Empirical evidence on developer’s commit activity for open-source software projects. In: Proceedings of the 25th International Conference on Software Engineering and Knowledge Engineering. 2013, 455–460
7
Tiwari P, Li W, Alomainy R, Wei B Y. An empirical study of different types of changes in the eclipse project. The Open Software Engineering Journal, 2013, 7: 24–37 https://doi.org/10.2174/1874107X01307010024
8
Kemerer C F, Slaughter S A. An empirical approach to studying software evolution. IEEE Transactions on Software Engineering, 1999, 25(4): 493–509 https://doi.org/10.1109/32.799945
9
Bennett K H. Software maintenance and evolution: a roadmap. In: Proceedings of the 22nd International Conference on Software Engineering. 2000, 73–78 https://doi.org/10.1145/336512.336534
10
Gupta A, Conradi R, Shull F, Cruzes D, Ackermann C, Rønneberg H, Landre E. Experience report on the effect of software development characteristics on change distribution. In: Proceedings of the 9th International Conference on Product Focused Software Process Improvement. 2008, 158–173 https://doi.org/10.1007/978-3-540-69566-0_15
11
Smith N, Capiluppi A, Ramil J F. A study of open source software evolution data using qualitative simulation. Software Process: Improvement and Practice, 2005, 10(3): 287–300 https://doi.org/10.1002/spip.230
12
Gonzalez-Barahona J, Robles G, Herriaz I, Ortega F. Studying the laws of software evolution in a long-lived FLOSS project. Journal of Software: Evolution and Process, 2014, 26(7): 589–612 https://doi.org/10.1002/smr.1615
13
Koch S. Evolution of open source software systems–a large-scale investigation. In: Proceedings of the 1st International Conference on Open Source Systems. 2005, 148–153
14
Schach S R, Jin B, Wright D R, Heller G Z, Offutt J. Determining the distribution of maintenance categories: survey versus measurement. Empirical Software Engineering, 2003, 8(4): 351–365 https://doi.org/10.1023/A:1025368318006
15
Burch E, Kungs H J. Modeling software maintenance requests: acase study. In: Proceedings of the International Conference on Software Maintenance. 1997, 40–47 https://doi.org/10.1109/ICSM.1997.624229
16
Swanson B. The dimensions of maintenance. In: Proceedings of the 2nd International Conference on Software Engineering. 1976, 492–497
17
IEEE. Standard for Software Maintenance (IEEE Std 1219–1998). New York: Institute for Electrical and Electronic Engineers, 1998
Lientz B P, Swanson E B, Tompkins G E. Characteristics of application software maintenance. Communication of the ACM, 1978, 21(6): 466–471 https://doi.org/10.1145/359511.359522
20
Nosek J, Palvia T P. Software maintenance management: changes in the last decade. Journal of Software Maintenance: Research and Practice, 1990, 2(3): 157–174 https://doi.org/10.1002/smr.4360020303
21
Lee M G, Jefferson T L. An empirical study of software maintenance of a Web-based Java application. In: Proceedings of the 21st IEEE International Conference on Software Maintenance. 2005, 571–576 https://doi.org/10.1109/ICSM.2005.19
22
Basili V, Briand L C, Condon S, Kim Y M, Melo W L, Valettt J D. Understanding and predicting the process of software maintenance releases. In: Proceedings of the 18th International Conference on Software Engineering. 1996, 464–474 https://doi.org/10.1109/ICSE.1996.493441
23
Sousa M J C, Moreira H M. A Survey on the software maintenance process. In: Proceedings of IEEE International Conference on Software Maintenance. 1998, 265–274 https://doi.org/10.1109/ICSM.1998.738518
24
Yip S W L, Lam T. A software maintenance survey. In: Proceedings of the 1st Asia-Pacific Software Engineering Conference. 1994, 70–79 https://doi.org/10.1109/APSEC.1994.465272
25
Abran A, Nguyenkim H. Analysis of maintenance work categories through measurement. In: Proceedings of IEEE Conference on Software Maintenance. 1991, 104–113. https://doi.org/10.1109/ICSM.1991.160315
26
Gefen D, Schneberger S L. The non-homogeneous maintenance periods: a case study of software modifications. In: Proceedings of IEEE Conference on Software Maintenance. 1996, 134–141 https://doi.org/10.1109/ICSM.1996.564998
27
Meqdadi O, Alhindawi N, Collard M L, Maletic J I. Towards understanding large-scale adaptive changes from version histories. In: Proceedings of the 29th IEEE International Conference on Software Maintenance. 2013, 416–419 https://doi.org/10.1109/ICSM.2013.61
28
Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. Journal of Machine Learning Research. 2003, 3: 993–1022
29
Kim S, Whitehead E J, Zhang Y. Classifying software changes: clean or buggy. IEEE Transactions on Software Engineering, 2008, 34(2): 181–196 https://doi.org/10.1109/TSE.2007.70773
30
Lehnert S, Riebisch M. A taxonomy of change types and its application in software evolution. In: Proceedings of the 19th International Conference and Workshops on Engineering of Computer Based Systems. 2012, 98–107 https://doi.org/10.1109/ECBS.2012.9
31
Chaplin N, Hale J E, Khan K M, Ramil J F, Tan W G. Types of software evolution and software maintenance. Journal of Software Maintenance and Evolution: Research and Practice, 2001, 13(1): 3–30 https://doi.org/10.1002/smr.220
32
Forward A, Lethbridge T C. A taxonomy of software types to facilitate search and evidence-based software engineering. In: Proceedings of the 2008 Conference of the Center for Advanced Studies on Collaborative Research: Meeting of Minds. 2008, 14 https://doi.org/10.1145/1463788.1463807
33
Saini M, Kaur K. Analyzing the change profiles of software systems using their change logs. International Journal of Software Engineering- Egypt, 2014, 7(2): 39–66
34
Larose D T. K-nearest neighbor algorithm. Discovering Knowledge in Data: An Introduction to Data Mining, 2005, 90–106
Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of International Joint Conference on Artificial Intelligence. 1995, 1137–1145
37
Cleveland W S. LOWESS: a program for smoothing scatterplots by robust locally weighted regression. The American Statistician, 1981, 35(1): 54 https://doi.org/10.2307/2683591
38
Massart D L, Smeyers-Verbeke A J, Capron A X, Schlesier K B. Visual presentation of data by means of box plots. LC-GC Europe, 2005, 18(4): 2–5
39
Ramsay J O, Silverman B W. Applied Functional Data Analysis: Methods and Case Studies. New York: Springer-Verlag, 2002 https://doi.org/10.1007/b98886
40
Cuesta-Albertos J A, Gordaliza A, Matrán C. Trimmed k-means: an attempt to robustify quantizers. The Annals of Statistics, 1997, 25(2): 553–576 https://doi.org/10.1214/aos/1031833664
41
Han J, Kamber M. Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann, 2000
Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of International Joint Conference on Artificial Intelligence. 1995, 1137–1145
44
Moore D S. Chi-square tests. Purdue University, 1976
45
Bolstad B M, Irizarry R A, Åstrand M, Speed T P.A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19(2): 185–193 https://doi.org/10.1093/bioinformatics/19.2.185
