Abstract
Software products can only be improved if we have a good understanding of the faults they typically contain. Code faults are a significant source of software product problems which we currently do not understand sufficiently. Open source change repositories are potentially a rich and valuable source of fault data for both researchers and practitioners. Such fault data can be used to better understand current product problems so that we can predict and address future product problems. However extracting fault data from change repositories is difficult. In this paper we compare the performance of three approaches to extracting fault data from the change repository of the Barcode Open Source System. Our main findings are that we have most confidence in our manual evaluation of diffs to identify fault fixing changes. We had less confidence in the ability of the two automatic approaches to separate fault fixing from non-fault fixing changes. We conclude that it is very difficult to reliably extract fault fixing data from change repositories, especially using automatic tools and that we need to be cautious when reporting or using such data.
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References
Levinson, M.: Let’s stop wasting $78 billion a year. CIO Magazine (2001)
Runeson, P., Andrews, A.: Detection or Isolation of Defects? An Experimental Comparison of Unit Testing and Code Inspection. In: ISSRE 2003, pp. 3–13 (2003)
Di Fatta, G., Leue, S., Stegantova, E.: Dis-criminative Pattern Mining in Software Fault Detection. In: SOQUA Workshop (2006)
Turhan, B., Kocak, G., Bener, A.: Data mining source code for locating software bugs: A case study in telecommunication industry. Expert Syst. Appl. 36, 6 (2009)
Bezerra, M.E.R., Oliveira, A.L.I., Adeodato, P.J.L., Meira, S.R.L.: Enhancing RBF-DDA Algorithm’s Robustness: Neural Networks Applied to Prediction of Fault-Prone Software Modules. In: Artificial Intelligence in Theory and Practice II (2007)
Oral, A.D., Bener, A.: Defect prediction for embedded software. In: Proceedings of the 22nd International Symposium on Computer and Information Sciences, pp. 1–6 (2007)
Pai, G.J., Dugan, J.B.: Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods. IEEE Trans. Software Eng. 33(10), 675–686 (2007)
Tomaszewski, P., Håkansson, J., Grahn, H., Lundberg, L.: Statistical models vs. expert estimation for fault prediction in modified code – An industrial case study. Journal of Systems and Software 80(8), 1227–1238 (2007)
Zimmermann, T., Premraj, R., Zeller, A.: Predicting defects for eclipse. In: Proceedings of the Third International Workshop on Predictor Models in Software Engineering (2007)
Sliwerski, J., Zimmermann, T., Zeller, A.: When do changes induce fixes? In: Proceedings of the Second International Workshop on Mining Software Repositories, pp. 24–28 (2005)
Schröter, A., Zimmermann, T., Premraj, R., Zeller, A.: Where do bugs come from? SIGSOFT Softw. Eng. Notes 31(6), 1–2 (2006)
Weyuker, E.J., Ostrand, T.J.: Comparing methods to identify defect reports in a change management database. In: DEFECTS 2008: Proceedings of the 2008 workshop on Defects in large software systems, pp. 27–31 (2008)
Ostrand, T.J., Weyuker, E.J., Bell, R.M.: Predicting the location and number of faults in large software systems. IEEE Trans. Software Eng. 31(4), 340–355 (2005)
Zimmermann, T., Weissgerber, P.: Preprocessing cvs data for fine-grained analysis. In: Proceedings of the First International Workshop on Mining Software Repositories, pp. 2–6 (2004)
Meyers, T.M., Binkley, D.: An empirical study of slice-based cohesion and coupling metrics. ACM Trans. Softw. Eng. Methodol. 17(1), 1–27 (2007)
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Hall, T., Bowes, D., Liebchen, G., Wernick, P. (2010). Evaluating Three Approaches to Extracting Fault Data from Software Change Repositories. In: Ali Babar, M., Vierimaa, M., Oivo, M. (eds) Product-Focused Software Process Improvement. PROFES 2010. Lecture Notes in Computer Science, vol 6156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13792-1_10
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DOI: https://doi.org/10.1007/978-3-642-13792-1_10
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