TY  - JOUR
T1  - Predicting Co-Changed Files: An External, Conceptual Replication
AU  - Tosun, Ayşe
AU  - Romero, Betül
PY  - 2019
DA  - June
DO  - 10.18466/cbayarfbe.489291
JF  - Celal Bayar University Journal of Science
JO  - CBUJOS
PB  - Manisa Celal Bayar Üniversitesi
WT  - DergiPark
SN  - 1305-130X
SP  - 161
EP  - 169
VL  - 15
IS  - 2
LA  - en
AB  - A software project canbe comprised of several, highly connected files. A software developer may notknow the files that are connected to which are developed or that are changed byanother developer. This may induce faults by missing necessary edits on allrelated files. We build a prediction model for identifying files that should beedited together during a code change, and evaluate the performance of our modelon two Apache projects’ development history over more than 10 years. We conductan external, conceptual replication study based on Wiese et al.'s prior work onpredicting co-changed files. Our study shares the same goal but differentiatesthe experimental design in terms of data set construction, selection of filepairs, feature selection and the model output. Our prediction model’s results,although the same performance measures are used, are much lower than what isreported in Wiese et al.’s study, mainly due to the differences in calculatingthese measures. The models evaluated at commit granularity could achieve 20%and 45% lower recall and precision rates, respectively, than those aggregatedover all file-pairs. Although it ispractically more useful, predicting all files that will be co-changed togetherduring a commit is more challenging than predicting whether a particular filewill be changed in that commit. More information about the context of aco-change, the degree of centrality of a file in the project, or projectcharacteristics could reveal more insights in building such predictors in thefuture.
KW  - Mining software repositories
KW  - association rule mining
KW  - predicting co-changed files
KW  - replication study
KW  - replication study
CR  - 1.	Borg, M., Wnuk, K., Regnell, B., Runeson, P. 2017. Supporting change impact analysis using a recommendation system: an industrial case study in a safety- critical context. IEEE Transaction on Software Engineering, 43(3): 675-700.
CR  - 2.	Kagdi, H., Maletic, J.I. Combining Single-version and Evolutionary Dependencies for Software-change Prediction, proceedings of the Fourth International Workshop on Mining Software Repositories (MSR), Minneapolis, USA, 2007, pp 17.
CR  - 3.	Wiese, I.S., Ré, R., Steinmacher, I., Kuroda, R.T, Oliva, G.A., Treude C., Gerosa, M.A. 2017. Using contextual information to predict co-changes. Journal of Systems and Software, 128: 220-235.
CR  - 4.	Ball, T., Kim, J., Porter, A.A., Siy, H.P. If Your Version Control System Could Talk, proceedings of the ICSE Workshop on Process Modeling and Empirical Studies of Software Engineering, 1997.
CR  - 5.	Gall, H., Hajek K., Jazayeri, M. Detection of Logical Coupling Based on Product Release History, proceedings of the International Conference on Software Maintenance (ICSM), Washington, DC, USA, 1998, pp. 190-198.
CR  - 6.	Zimmermann, T., Weisgerber, P., Diehl, S., Zeller, A. Mining Version Histories to Guide Software Changes, proceedings of the 26th International Conference on Software Engineering (ICSE), Washington, DC, USA, 2004, pp. 563-572.
CR  - 7.	Canfora, G., Cerulo, L., Cimitile, M., Penta, M.D. 2014. How changes affect software entropy: an empirical study. Empirical Software Engineering, 19(1): 1-38.
CR  - 8.	Hassan, A.E., Holt, R.C. Predicting Change Propagation in Software Systems, proceedings of the 20th IEEE International Conference on Software Maintenance, Chicago, IL, USA, 2004, pp. 284-293.
CR  - 9.	Macho, C., McIntosh, S., Pinzger, M. Predicting Build Co-changes with Source Code Change and Commit Categories, proceedings of the 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), Suita, Japan, 2016, pp. 541-551.
CR  - 10.	Kouroshfa, E. Studying the Effect of Co-change Dispersion on Software Quality, proceedings of the International Conference on Software Engineering (ICSE), Piscataway, USA, 2013, pp. 1450-1452.
CR  - 11.	Meyer, B., Nordio M. Empirical Software Engineering and Verification: International Summer Schools; Springer-Verlag: Berlin, Heidelberg, 2012.
CR  - 12.	Shepperd, M., Ajienka, N., Counsell, S. 2018. The role and value of replication in empirical software engineering results. Information and Software Technology, 99: 120-132.
CR  - 13.	Silva, F.Q., Suassuna, M., França, A.C., Grubb, A.M., Gouveia, T.B., Monteiro, C.V., Santos, I.E. 2014. Replication of empirical studies in software engineering research: a systematic mapping study. Empirical Software Engineering, 19(3): 501-557.
CR  - 14.	Shull, F., Carver, J., Vegas, S., Juristo, N. 2008. The role of replications in empirical software engineering. Empirical Software Engineering, 13: 211-218.
UR  - https://doi.org/10.18466/cbayarfbe.489291
L1  - http://dergipark.org.tr/tr/download/article-file/747718
ER  -