A Systematic Mapping Study on Analysis of Code Repositories
Volume 32, Issue 3 (2021), pp. 619–660
Pub. online: 2 June 2021
Type: Research Article
Open Access
Open Access
Received
1 October 2020
1 October 2020
Accepted
1 May 2021
1 May 2021
Published
2 June 2021
2 June 2021
Abstract
Code repositories contain valuable information, which can be extracted, processed and synthesized into valuable information. It enabled developers to improve maintenance, increase code quality and understand software evolution, among other insights. Certain research has been made during the last years in this field. This paper presents a systematic mapping study to find, evaluate and investigate the mechanisms, methods and techniques used for the analysis of information from code repositories that allow the understanding of the evolution of software. Through this mapping study, we have identified the main information used as input for the analysis of code repositories (commit data and source code), as well as the most common methods and techniques of analysis (empirical/experimental and automatic). We believe the conducted research is useful for developers working on software development projects and seeking to improve maintenance and understand the evolution of software through the use and analysis of code repositories.
References
Abdalkareem, R. Shihaba, E., Rillingb, J. (2017). On code reuse from StackOverflow: an exploratory study on Android apps. Information and Software Technology, 88, 148–158. https://doi.org/10.1016/j.infsof.2017.04.005.
Abdeen, H. Bali, K., Sahraoui, H., Dufour, B. (2015). Learning dependency-based change impact predictors using independent change histories. Information and Software Technology, 67, 220–235. https://doi.org/10.1016/j.infsof.2015.07.007.
Abuasad, A., Alsmadi, I.M. (1994, (2012)). The correlation between source code analysis change recommendations and software metrics. In: ICICS ’12: Proceedings of the 3rd International Conference on Information and Communication Systems. https://doi.org/10.1145/2222444.2222446.
Agarwal, H., Husain, F., Saini, P. (2019). Next generation noise and affine invariant video watermarking scheme using Harris feature extraction. In: Third International Conference, ICACDS 2019, Ghaziabad, India, April 12–13, 2019, Revised Selected Papers, Part II, Advances in Computing and Data Sciences. Springer, Singapore, pp. 655–665. https://doi.org/10.1007/978-981-13-9942-8.
de Almeida Biolchini, J.C., Mian, P.G., Natali, A.C.C., Conte, T.U., Travassos, G.H. (2007). Scientific research ontology to support systematic review in software engineering. Advanced Engineering Informatics, 21(2), 133–151. https://doi.org/10.1016/j.aei.2006.11.006.
Amann, S., Beyer, S., Kevic, K., Gall, H. (2015). Software mining studies: goals, approaches, artifacts, and replicability. In: Meyer, B., Nordio, M. (Eds.), Software Engineering. LASER 2013, LASER 2014, Lecture Notes in Computer Science, Vol. 8987. Springer, Cham. https://doi.org/10.1007/978-3-319-28406-4_5.
Arora, R., Garg, A. (2018). Analysis of software repositories using process mining. Smart Computing and Informatics Smart Innovation, Systems and Technologies, 78, 637–643. https://doi.org/10.1007/978-981-10-5547-8_65.
Badampudi, D., Wohlin, C., Petersen, K. (2016). Software component decision-making: in-house, OSS, COTS or outsourcing – a systematic literature review. Journal of Systems and Software, 121, 105–124. https://doi.org/10.1016/j.jss.2016.07.027.
Bailey, J., Budgen, D., Turner, M., Kitchenham, B., Brereton, P., Linkman, S. (2007). Evidence relating to object-oriented software design: a survey. In: Proceedings of the First International Symposium on Empirical Software Engineering and Measurement. IEEE Computer Society, USA, pp. 482–484. https://doi.org/10.1109/ESEM.2007.46.
Ball, T., Kim J., Siy H.P., (1997). If your version control system could talk. In: ICSE Workshop on Process Modelling and Empirical Studies of Software Engineering https://doi.org/10.1.1.48.910.
Baltrusaitis, T. Ahuja, C., Morency, L. (2019). Multimodal machine learning: a survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443. https://doi.org/10.1109/TPAMI.2018.2798607.
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., Herrera, F. (2020). Explainable Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012.
Borg, M., Runeson, P., Ardö A. (2014). Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability. Empirical Software Engineering, 19(6), 1565–1616. https://doi.org/10.1007/s10664-013-9255-y.
Borges, H., Tulio Valente, M. (2018). What’s in a GitHub Star? Understanding repository starring practices in a social coding platform. Journal of Systems and Software, 146, 112–129. https://doi.org/10.1016/j.jss.2018.09.016.
Cavalcanti, Y.C. da Mota Silveira Neto, P.A., do Carmo Machado, I., Vale, T.F., de Almeida, E.S., de Lemos Meira, S.R. (2014). Challenges and opportunities for software change request repositories: a systematic mapping study. Journal of Software: Evolution and Process, 26(7), 620–653. https://doi.org/10.1002/smr.1639.
Chahal, K.K., Saini, M. (2016). Open source software evolution: a systematic literature review (Part 1). International Journal of Open Source Software and Processes, 7(1), 1–27. https://doi.org/10.4018/IJOSSP.2016010101.
Chaturvedi, K.K., Sing, V.B., Singh, P. (2013). Tools in mining software repositories. In: Proceedings of the 2013 13th International Conference on Computational Science and Its Applications, ICCSA 2013, pp. 89–98. https://doi.org/10.1109/ICCSA.2013.22.
Chen, T.H. Thomas, S.W., Hassan, A.E. (2016). A survey on the use of topic models when mining software repositories. Empirical Software Engineering. https://doi.org/10.1007/s10664-015-9402-8.
Cornelissen, B. Zaidman, A., van Deursen, A., Moonen, L., Koschke, R. (2009). A systematic survey of program comprehension through dynamic analysis. IEEE Transactions on Software Engineering, 35(5), 684–702. https://doi.org/10.1109/TSE.2009.28.
Cosentino, V., Cánovas Izquierdo J.L. Cabot J. (2017). A systematic mapping study of software development with GitHub. IEEE Access, 5, 7173–7192. https://doi.org/10.1109/ACCESS.2017.2682323.
Costa, C., Murta, L. (2013). Version control in Distributed Software Development: a systematic mapping study. In: IEEE 8th International Conference on Global Software Engineering, ICGSE 2013, pp. 90–99. https://doi.org/10.1109/ICGSE.2013.19.
Datta, S., Datta, S., Naidu, K.V.M. (2012). Capacitated team formation problem on social networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1005–1013. https://doi.org/10.1145/2339530.2339690.
De Farias, Novais, M.A.F.R., Colaço Júnior, M., da Silva Carvalho, L.P. (2016). A systematic mapping study on mining software repositories. In: SAC ’16: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 1472–1479. https://doi.org/10.1145/2851613.2851786.
Del Carpio, P.M. (2017). Extracción de Nubes de Palabras en Repositorios Git. 2017 12th Iberian Conference on Information Systems and Technologies (CISTI) https://doi.org/10.23919/CISTI.2017.7975911.
Demeyer, S. Murgia, A., Wyckmans, K., Lamkanfi, A. (2013). Happy birthday! A trend analysis on past MSR papers. In: 2013 10th Working Conference on Mining Software Repositories (MSR), pp. 353–362. https://doi.org/10.1109/MSR.2013.6624049.
Dias de Moura, M.H., Dantas do Nascimento H.A., Couto Rosa T. (2014). Extracting new metrics from version control system for the comparison of software developers. In: ARES ’14: Proceedings of the 2014 Ninth International Conference on Availability, Reliability and Security, pp. 41–50. https://doi.org/10.1109/SBES.2014.25.
Dias, M., Bacchelli, A., Gousios, G., Cassou, D., Ducasse, S., (2015). Untangling fine-grained code changes. In: 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER), pp. 341–350. https://doi.org/10.1109/SANER.2015.7081844.
Dit, B., Revelle, M., Gethers, M., Poshyvanyk, D. (2013). Feature location in source code: a taxonomy and survey. Journal of Software: Evolution and Process, 25(1), 53–95. https://doi.org/10.1002/smr.567.
Dyer, R., Nguyen, H.A., Rajan, H., Nguyen, T.N. (2015). Boa: Ultra-large-scale software repository and source-code mining. ACM Transactions on Software Engineering and Methodology, 25, 1. https://doi.org/10.1145/2803171.
Elsen, S. (2013). VisGi: visualizing Git branches. 2013 First IEEE Working Conference on Software Visualization (VISSOFT). https://doi.org/10.1109/VISSOFT.2013.6650522.
Falessi, D., Reichel, A. (2015). Towards an open-source tool for measuring and visualizing the interest of technical debt. In: 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD), pp. 1–8. https://doi.org/10.1109/MTD.2015.7332618.
Farias, M., Novais, R., Ortins, P., Colaço, M., Mendonça, M. (2015). Analyzing distributions of emails and commits from OSS contributors through mining software repositories: an exploratory study. In: ICEIS 2015: Proceedings of the 17th International Conference on Enterprise Information Systems, Vol. 2, pp. 303–310. https://doi.org/10.5220/0005368603030310.
Finlay, J. Pears, R., Connor, A.M. (2014). Data stream mining for predicting software build outcomes using source code metrics. Information and Software Technology, 56(2), 183–198. https://doi.org/10.1016/j.infsof.2013.09.001.
Foucault, M., Palyart, M., Blanc, X., Murphy, G.C., Falleri, J.-R. (2015). Impact of developer turnover on quality in open-source software. In: ESEC/FSE 2015: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, pp. 829–841. https://doi.org/10.1145/2786805.2786870.
Franco-Bedoya, O. Ameller, D., Costal, D., Franch, X. (2017). Open source software ecosystems: a systematic mapping. Information and Software Technology, 91, 160–185. https://doi.org/10.1016/j.infsof.2017.07.007.
Fu, Y., Yan, M., Zhang, X., Xu, L., Yang, D., Kymer, J.D. (2015). Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation. Information and Software Technology, 57(1), 369–377. https://doi.org/10.1016/j.infsof.2014.05.017.
Gamalielsson, J., Lundell, B. (2014). Sustainability of Open Source software communities beyond a fork: How and why has the LibreOffice project evolved? Journal of Systems and Software, 89(1), 128–145. https://doi.org/10.1016/j.jss.2013.11.1077.
Gani, A. Siddiqa, A., Shamshirband, S., Hanum, F. (2016). A survey on indexing techniques for big data: taxonomy and performance evaluation. Knowledge and Information Systems, 46(2), 241–284. https://doi.org/10.1007/s10115-015-0830-y.
Genero, M., Fernandez, A.M., James Nelson, H., Poels, G. (2011). A systematic literature review on the quality of UML models. Journal of Database Management, 22(3), 46–66. https://doi.org/10.4018/jdm.2011070103.
Grossi, V., Romei, A., Turini, F. (2017). Survey on using constraints in data mining. Data Mining and Knowledge Discovery, 31(2), 424–464. https://doi.org/10.1007/s10618-016-0480-z.
Güemes-Peña, D., López-Nozal, C., Marticorena-Sánchez, R. (2018). Emerging topics in mining software repositories: machine learning in software repositories and datasets. Progress in Artificial Intelligence, 7, 237–247. https://doi.org/10.1007/s13748-018-0147-7.
Gupta, M., Sureka, A., Padmanabhuni, S. (2014). Process mining multiple repositories for software defect resolution from control and organizational perspective. In: MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 122–131. https://doi.org/10.1145/2597073.2597081.
Haddaway, N.R., Macura, B., Whaley, P. (2018). ROSES Reporting standards for Systematic Evidence Syntheses: Pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps. Environmental Evidence, 7(1), 4–11. https://doi.org/10.1186/s13750-018-0121-7.
Harman, M. (2012). The role of artificial intelligence in software engineering. In: 2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE). IEEE. https://doi.org/10.1109/RAISE.2012.6227961.
Hassan, A.E. (2008). The road ahead for mining software repositories. In: 2008 Frontiers of Software Maintenance, pp. 48–57. https://doi.org/10.1109/FOSM.2008.4659248. 2008.
Herzig, K., Just S., Zeller A., (2016). The impact of tangled code changes on defect prediction models. Empirical Software Engineering , 21(2), 303–336. https://doi.org/10.1007/s10664-015-9376-6.
Hidalgo Suarez, C.G., Bucheli, V.A., Restrepo-Calle, F., Gonzalez, F.A. (2018). A strategy based on technological maps for the identification of the state-of-the-art techniques in software development projects: Virtual judge projects as a case study. In: Serrano, C.J., Martínez-Santos, J. (Eds.), Advances in Computing. CCC 2018, Communications in Computer and Information Science, Vol. 885. Springer, Cham. https://doi.org/10.1007/978-3-319-98998-3_27.
Ivarsson, M., Gorschek, T. (2011). A method for evaluating rigor and industrial relevance of technology evaluations. Empirical Software Engineering, 16(3), 365–395. https://doi.org/10.1007/s10664-010-9146-4.
Jarczyk, O., Jaroszewicz, S., Wierzbicki, A., Pawlak, K., Jankowski-Lorek, M. (2017). Surgical teams on GitHub: modeling performance of GitHub project development processes. Information and Software Technology, 100, 32–46. https://doi.org/10.1016/j.infsof.2018.03.010. 2018.
Jiang, J., Lo, D., Zheng, J., Xia, X., Yang, Y., Zhang, L., (2019). Who should make decision on this pull request? Analyzing time-decaying relationships and file similarities for integrator prediction. Journal of Systems and Software, 154, 196–210. https://doi.org/10.1016/j.jss.2019.04.055.
Joblin, M., Apel, S., Riehle, D., Mauerer, W., Siegmund, J. (2015). From developer networks to verified communities: a fine-grained approach. In: 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE), pp. 563–573. https://doi.org/10.1109/ICSE.2015.73.
Joy, A., Thangavelu, S., Jyotishi, A. (2018). Performance of GitHub open-source software project: an empirical analysis. In: 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC), pp. 1–6. https://doi.org/10.1109/ICAECC.2018.8479462.
Just, S., Herzig, K., Czerwonka, J., Murphy, B. (2016). Switching to git: the good, the bad, and the ugly. In: 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE), pp. 400–411. https://doi.org/10.1109/ISSRE.2016.38.
Kagdi, H., Collard, M.L., Maletic, J.I. (2007). A Survey and Taxonomy of Approaches for Mining Software Repositories in the Context of Software Evolution. Journal of Software: Evolution and Process, 19(2), 77–131. https://doi.org/10.1002/smr.344.
Kagdi1, H., Gethers, M., Poshyvanyk, D., Hammad, M. (2014). Assigning change requests to software developers. Journal of Software: Evolution and Process, 26(12), 1172–1192. https://doi.org/10.1002/smr.530.
Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D.M., Damian, D. (2016). An in-depth study of the promises and perils of mining GitHub. Empirical Software Engineering, 21(5), 2035–2071. https://doi.org/10.1007/s10664-015-9393-5.
Kasurinen, J., Knutas, A. (2018). Publication trends in gamification: a systematic mapping study. Computer Science Review, 27, 33–44. https://doi.org/10.1016/j.cosrev.2017.10.003.
Kirinuki, H., Higo, Y., Hotta, K., Kusumoto, S. (2014). Hey! Are you committing tangled changes? In: ICPC 2014: Proceedings of the 22nd International Conference on Program Comprehension, pp. 262–265. https://doi.org/10.1145/2597008.2597798.
Kitchenham, B. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. https://doi.org/10.1145/1134285.1134500.
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., Linkman, S. (2009). Systematic literature reviews in software engineering – a systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009.
Kitchenham, B., Sjøberg, D.I.K., Dyba, T., Pearl Brereton, O., Budgen, D., Höst, M. (2013). Trends in the quality of human-centric software engineering experiments – a quasi-experiment. IEEE Transactions on Software Engineering, 39(7), 1002–1017. https://doi.org/10.1109/TSE.2012.76.
Kitchenham, B.A., Budgen, D., Pearl Brereton, O. (2011). Using mapping studies as the basis for further research – a participant-observer case study. Information and Software Technology, 53(6), 638–651. https://doi.org/10.1016/j.infsof.2010.12.011.
Kuhrmann, M., Méndez Fernández, D., Daneva, M. (2017). On the pragmatic design of literature studies in software engineering: an experience-based guideline. Empirical Software Engineering, 22(6), 2852–2891. https://doi.org/10.1007/s10664-016-9492-y.
Kumar, L., Sripada, S.K., Sureka, A., Rath, S.K. (2018). Effective fault prediction model developed using Least Square Support Vector Machine (LSSVM). Journal of Systems and Software, 137, 686–712. https://doi.org/10.1016/j.jss.2017.04.016.
Laukkanen, E., Itkonen, J., Lassenius, C. (2017). Problems, causes and solutions when adopting continuous delivery—a systematic literature review. Information and Software Technology, 82, 55–79. https://doi.org/10.1016/j.infsof.2016.10.001.
Lee, H., Seo, B., Seo, E. (2013). A git source repository analysis tool based on a novel branch-oriented approach. In: 2013 International Conference on Information Science and Applications (ICISA), pp. 1–4. https://doi.org/10.1109/ICISA.2013.6579457.
Li, H.Y., Li, M., Zhou, Z.-H. (2019). Towards one reusable model for various software defect mining tasks. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 11441 LNAI, 212–224. https://doi.org/10.1007/978-3-030-16142-2_17.
Liu, J., Li, J., He, L. (2016). A comparative study of the effects of pull request on GitHub projects. In: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), pp. 313–322. https://doi.org/10.1109/COMPSAC.2016.27.
Malheiros, Y., Moraes, A., Trindade, C., Meira, S. (2012). A source code recommender system to support newcomers. In: COMPSAC ’12: Proceedings of the 2012 IEEE 36th Annual Computer Software and Applications Conference, pp. 19–24. https://doi.org/10.1109/COMPSAC.2012.11.
Maqsood, J., Eshraghi, I., Sarmad Ali, S. (2017). Success or failure identification for GitHub’s open source projects. In: ICMSS ’17: Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences, pp. 145–150. https://doi.org/10.1145/3034950.3034957.
Martínez-Torres, M.R., Toral, S.L., Barrero, F.J., Gregor, D. (2013). A text categorisation tool for open source communities based on semantic analysis. Behaviour & Information Technology, 32(6), 532–544. https://doi.org/10.1080/0144929X.2011.624634.
Munir, H., Wnuk K., Runeson P. (2016). Open innovation in software engineering: a systematic mapping study. Empirical Software Engineering, 21(2), 684–723. https://doi.org/10.1007/s10664-015-9380-x.
Murgia, A., Tourani, P., Adams, B., Ortu, M. (2014). Do developers feel emotions? An exploratory analysis of emotions in software artifacts. In: MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 262–271. https://doi.org/10.1145/2597073.2597086.
Negara, S., Codoban M., Dig D., Johnson R.E. (2014). Mining fine-grained code changes to detect unknown change patterns. In: ICSE 2014: Proceedings of the 36th International Conference on Software Engineering, pp. 803–813. https://doi.org/10.1145/2568225.2568317.
Nishi, M.A., Damevski, K. (2018). Scalable code clone detection and search based on adaptive prefix filtering. Journal of Systems and Software, 137, 130–142. https://doi.org/10.1016/j.jss.2017.11.039.
Novielli, N., Girardi D., Lanubile F. (2018). A benchmark study on sentiment analysis for software engineering research. In: MSR ’18: Proceedings of the 15th International Conference on Mining Software Repositories, pp. 364–375. https://doi.org/10.1145/3196398.3196403.
Ozbas-Caglayan, K., Dogru, A.H. (2013). Software repository analysis for investigating design-code compliance. In: 2013 Joint Conference of the 23rd International Workshop on Software Measurement and the 8th International Conference on Software Process and Product Measurement, pp. 231–233. https://doi.org/10.1109/IWSM-Mensura.2013.40.
Pedreira, O., García, F., Brisaboa, N., Piattini, M. (2015). Gamification in software engineering – a systematic mapping. Information and Software Technology, 57(1), 157–168. https://doi.org/10.1016/j.infsof.2014.08.007.
Perez-Castillo, R., Ruiz-Gonzalez, F., Genero, M., Piattini, M. (2019). A systematic mapping study on enterprise architecture mining A systematic mapping study on enterprise architecture mining. Enterprise Information Systems, 13(5), 675–718. https://doi.org/10.1080/17517575.2019.1590859.
Petersen, K., Gencel, C. (2013). Worldviews, research methods, and their relationship to validity in empirical software engineering research. In: 2013 Joint Conference of the 23rd International Workshop on Software Measurement and the 8th International Conference on Software Process and Product Measurement, pp. 81–89. https://doi.org/10.1109/IWSM-Mensura.2013.22.
Petersen, K., Vakkalanka S. Kuzniarz L. (2015). Guidelines for conducting systematic mapping studies in software engineering: an update. Information and Software Technology, 64, 1–18. https://doi.org/10.1016/j.infsof.2015.03.007.
Rosen, C., Grawi, B., Shihab, E. (2015). Commit guru: analytics and risk prediction of software commits. In: ESEC/FSE 2015: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, pp. 966–969. https://doi.org/10.1145/2786805.2803183. 2015.
Shamseer, L. Moher, D., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L.A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. The BMJ, 349, 1–25. https://doi.org/10.1136/bmj.g7647.
Siddiqui, T., Ahmad, A. (2018). Data mining tools and techniques for mining software repositories: a systematic review. Advances in Intelligent Systems and Computing, 654, 717–726. https://doi.org/10.1007/978-981-10-6620-7_70.
Stol, K.J., Ralph P., Fitzgerald B. (2016). Grounded theory in software engineering research: a critical review and guidelines In: ICSE ’16: Proceedings of the 38th International Conference on Software Engineering, pp. 120–131. https://doi.org/10.1145/2884781.2884833.
Tahir, A., Tosi, D., Morasca, S. (2013). A systematic review on the functional testing of semantic web services. Journal of Systems and Software, 86(11), 2877–2889. https://doi.org/10.1016/j.jss.2013.06.064.
van Eck, N.J., Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070. https://doi.org/10.1007/s11192-017-2300-7.
Waltman, L., van Eck N.J. Noyons Ed.C.M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635. https://doi.org/10.1016/j.joi.2010.07.002.
Wang, N., Liang, H. Jia, Y. Ge, S. Xue, Y. Wang, Z. (2016). Cloud computing research in the IS discipline: a citation/co-citation analysis. Decision Support Systems, 86, 35–47. https://doi.org/10.1016/j.dss.2016.03.006.
Wijesiriwardana, C., Wimalaratne, P. (2018). Fostering real-time software analysis by leveraging heterogeneous and autonomous software repositories. IEICE Transactions on Information and Systems E, 101D(11), 2730–2743. https://doi.org/10.1587/transinf.2018EDP7094.
Wu, Y., Kropczynski, J., Shih, P.C., Carroll, J.M. (2014). Exploring the ecosystem of software developers on GitHub and other platforms. In: CSCW Companion ’14: Proceedings of the companion publication of the 17th ACM conference on Computer Supported Cooperative Work & Social Computing, pp. 265–268. https://doi.org/10.1145/2556420.2556483.
Zolkifli, N.N., Ngah, A., Deraman, A. (2018). Version control system: a review. Procedia Computer Science, 135, 408–415. https://doi.org/10.1016/j.procs.2018.08.191.
Biographies
Sayago-Heredia Jaime
J. Sayago-Heredia is a PhD student at the University of Castilla-La Mancha (UCLM), Spain. His research interests include software engineering. He is a professor at the School of Systems and Computing of the Pontificia Universidad Católica del Ecuador, Sede Esmeraldas. Contact him at jaime.sayago@pucese.edu.ec.
Pérez-Castillo Ricardo
R. Perez-Castillo is a researcher at the Information Technologies and Systems Institute, University of Castilla-La Mancha (UCLM), Spain. His research interests include architecture-driven modernization, model-driven development, business-process archaeology, and enterprise architecture. Perez-Castillo received a PhD in computer science from UCLM. Contact him at ricardo.pdelcastillo@uclm.es.
Piattini Mario
M. Piattini is the director of the Alarcos Research Group and a full professor at the University of Castilla-La Mancha, Spain. His research interests include software and data quality, information-systems audit and security, and IT governance. Piattini received a PhD in computer science from Madrid Technical University, Spain. Contact him at mario.piattini@uclm.es.
