Abstract
Code reviews constitute an important activity in software quality assurance. Although they are essentially based on human expertise and scrupulosity, they can also be supported by automated tools. In this paper we present such a solution integrated with code review tools. It is based on a SVM classifier that indicates potentially buggy changes. We train such a classifier on the history of a project. In order to construct a training set, we assume that a change/commit is buggy if its modifications has been later altered by a bug-fix commit. We evaluated our approach on 77 selected projects taken from GitHub and achieved promising results. We also assessed the quality of the resulting classifier depending on the size of a project and the fraction of the history of a project that have been used to build the training set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arisholm, E., Briand, L.C., Johannessen, E.B.: A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Journal of Systems and Software 83(1), 2–17 (2010), http://dx.doi.org/10.1016/j.jss.2009.06.055
Bird, C., Rigby, P.C., Barr, E.T., Hamilton, D.J., Germán, D.M., Devanbu, P.T.: The promises and perils of mining git. In: Proceedings of the 6th International Working Conference on Mining Software Repositories, MSR 2009 (Co-located with ICSE), Vancouver, BC, Canada,, May 16-17, pp. 1–10 (2009), http://dx.doi.org/10.1109/MSR.2009.5069475
Catal, C., Diri, B.: A systematic review of software fault prediction studies. Expert Systems with Applications 36(4), 7346–7354 (2009)
D’Ambros, M., Lanza, M., Robbes, R.: An extensive comparison of bug prediction approaches. In: Proceedings of the 7th International Working Conference on Mining Software Repositories, MSR 2010 (Co-located with ICSE), Cape Town, South Africa, May 2-3, pp. 31–41 (2010), http://dx.doi.org/10.1109/MSR.2010.5463279
D’Ambros, M., Lanza, M., Robbes, R.: An extensive comparison of bug prediction approaches. In: Proceedings of the 7th International Working Conference on Mining Software Repositories, MSR 2010 (Co-located with ICSE), Cape Town, South Africa, May 2-3, 2010, Proceedings. pp. 31–41 (2010), http://dx.doi.org/10.1109/MSR.2010.5463279
Fejzer, M.: Commit classification application. Project on Github code repository (2014), https://github.com/mfejzer/CommitClassification
Hall, M.A., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009), http://doi.acm.org/10.1145/1656274.1656278
Jeong, G., Kim, S., Zimmermann, T., Yi, K.: Improving code review by predicting reviewers and acceptance of patches. Research on Software Analysis for Error-free Computing Center Tech-Memo (ROSAEC MEMO 2009-006) (2009)
Joachims, T.: Text categorization with support vector machines. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Kim, S.: Adaptive bug prediction by analyzing project history. University of California, Santa Cruz (2006)
Kim, S., Whitehead Jr., E.J., Zhang, Y.: Classifying software changes: Clean or buggy? IEEE Trans. Software Eng. 34(2), 181–196 (2008), http://doi.ieeecomputersociety.org/10.1109/TSE.2007.70773
Moors, A., Piessens, F., Odersky, M.: Parser combinators in scala. CW Reports (2008)
Nelson, S.D., Schumann, J.: What makes a code review trustworthy? In: 37th Hawaii International Conference on System Sciences (HICSS-37 2004), CD-ROM / Abstracts Proceedings, Big Island, HI, USA, January 5-8 (2004), http://dx.doi.org/10.1109/HICSS.2004.1265711
Ostrand, T.J., Weyuker, E.J., Bell, R.M.: Programmer-based fault prediction. In: Proceedings of the 6th International Conference on Predictive Models in Software Engineering, PROMISE 2010, Timisoara, Romania, September 12-13, p. 19 (2010), http://doi.acm.org/10.1145/1868328.1868357
Radjenovic, D., Hericko, M., Torkar, R., Zivkovic, A.: Software fault prediction metrics: A systematic literature review. Information & Software Technology 55(8), 1397–1418 (2013), http://dx.doi.org/10.1016/j.infsof.2013.02.009
Ray, B., Posnett, D., Filkov, V., Devanbu, P.T.: A large scale study of programming languages and code quality in github. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE-22), Hong Kong, China, November 16 - 22, pp. 155–165 (2014), http://doi.acm.org/10.1145/2635868.2635922
Shivaji, S., Whitehead Jr., E.J., Akella, R., Kim, S.: Reducing features to improve code change-based bug prediction. IEEE Trans. Software Eng. 39(4), 552–569 (2013), http://doi.ieeecomputersociety.org/10.1109/TSE.2012.43
Sliwerski, J., Zimmermann, T., Zeller, A.: When do changes induce fixes? In: Proceedings of the 2005 International Workshop on Mining Software Repositories, MSR 2005, Saint Louis, Missouri, USA, May 17 (2005), http://doi.acm.org/10.1145/1083142.1083147
Tian, Y., Lawall, J.L., Lo, D.: Identifying linux bug fixing patches. In: 34th International Conference on Software Engineering, ICSE 2012, Zurich, Switzerland, June 2-9, pp. 386–396 (2012), http://dx.doi.org/10.1109/ICSE.2012.6227176
Treude, C., Storey, M.A.D.: Work item tagging: Communicating concerns in collaborative software development. IEEE Trans. Software Eng. 38(1), 19–34 (2012), http://doi.ieeecomputersociety.org/10.1109/TSE.2010.91
Vapnik, V.: Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics). Springer-Verlag New York, Inc., Secaucus (1982)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Fejzer, M., Wojtyna, M., Burzańska, M., Wiśniewski, P., Stencel, K. (2015). Supporting Code Review by Automatic Detection of Potentially Buggy Changes. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. BDAS 2015. Communications in Computer and Information Science, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-18422-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-18422-7_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18421-0
Online ISBN: 978-3-319-18422-7
eBook Packages: Computer ScienceComputer Science (R0)