Abstract
The information about which modules of a future version of a software system are defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. However, constructing effective defect prediction models in an industrial setting involves a number of key questions. In this paper we discuss ten key questions identified in context of establishing defect prediction in a large software development project. Seven consecutive versions of the software system have been used to construct and validate defect prediction models for system test planning. Furthermore, the paper presents initial empirical results from the studied project and, by this means, contributes answers to the identified questions.
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.
References
Ostrand, T.J., Weyuker, E.J., Bell, R.M.: Predicting the Location and Number of Faults in Large Software Systems. IEEE Trans. on Software Engineering 31(4), 340–355 (2005)
Nagappan, N., Ball, T.: Use of Relative Code Churn Measures to Predict System Defect Density. In: 27th Int. Conf. on Software Engineering, St. Louis, MO, USA. ACM, New York (2005)
Nagappan, N., Ball, T., Zeller, A.: Mining Metrics to Predict Component Failures. In: 28th Int. Conf. on Software Engineering, Shanghai, China. ACM, New York (2006)
Subramanyam, R., Krishnan, M.: Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects. IEEE Trans. on Software Engineering 29(4), 297–310 (2003)
Li, P.L., Herbsleb, J., Shaw, M., Robinson, B.: Experiences and Results from Initiating Field Defect Prediction and Product Test Prioritization Efforts at ABB Inc. In: 28th Int. Conf. on Software Engineering, Shanghai, China. ACM, New York (2006)
Weyuker, E.J.: Software Engineering Research: From Cradle to Grave. In: 6th European Software Engineering Conference and ACM SIGSOFT Symposium on the Foundations of Software Engineering, Dubrovnik, Croatia. ACM, New York (2007)
Ramler, R., Wolfmaier, K.: Issues and Effort in Integrating Data from Heterogeneous Software Repositories and Corporate Databases. In: 2nd Int. Symposium on Empirical Software Engineering and Measurement, Kaiserslautern, Germany. ACM, New York (2008)
Wahyudin, D., Ramler, R., Biffl, S.: A Framework for Defect Prediction in Specific Software Project Contexts. In: 3rd IFIP TC2 Central and East European Conference on Software Engineering Techniques, Brno, Slovakia. Springer, Heidelberg (2008)
Menzies, T., Greenwald, J., Frank, A.: Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Trans. on Software Engineering 33, 2–13 (2007)
Khoshgoftaar, T.M., Seliya, N.: Analogy-Based Practical Classification Rules for Software Quality Estimation. Empirical Software Engineering 8(4), 325–350 (2003)
Kim, S., Whitehead Jr., E.J.: Classifying Software Changes: Clean or Buggy? IEEE Trans. on Software Engineering 34(2), 181–196 (2008)
Koru, A.G., Hongfang, L.: Building Defect Prediction Models in Practice. IEEE Software 22, 23–29 (2005)
Denaro, G., Pezze, M.: An empirical evaluation of fault-proneness models. In: 24th Int. Conf. on Software Engineering, Orlando, Florida. ACM, New York (2002)
Ramler, R.: The Impact of Product Development on the Lifecycle of Defects. In: Workshop on Defects in Large Software Systems, Seattle, WA, USA. ACM, New York (2008)
Kutlubay, O., Turhan, B., Bener, A.B.: A Two-Step Model for Defect Density Estimation. In: 33rd Euromicro Conf. on Software Eng. and Advanced Applications, Lübeck, Germany. IEEE, Los Alamitos (2007)
Moser, R., Pedrycz, W., Succi, G.: A Comparative Analysis of the Efficiency of Change Metrics and Static Code Attributes for Defect Prediction. In: 30th Int. Conf. on Software Engineering. ACM, New York (2008)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Turhan, B., Kocak, G., Bener, A.: Software Defect Prediction Using Call Graph Based Ranking (CGBR) Framework. In: 34th Euromicro Conf. on Software Engineering and Advanced Applications. IEEE, Los Alamitos (2008)
Lessmann, S., Baesens, B., Mues, C., Pietsch, S.: Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings. IEEE Trans. on Software Engineering 34(11), 485–496 (2008)
Menzies, T., Di Stefano, J., Ammar, K., McGill, K., Callis, P., Chapman, R., Davis, J.: When Can We Test Less? In: 9th Int. Symposium on Software Metrics, Sydney, Australia. IEEE, Los Alamitos (2003)
Koru, A.G., Tian, J.: An Empirical Comparison and Characterization of High Defect and High Complexity Modules. J. Systems and Software 67(3), 153–163 (2003)
Natschläger, T., Kossak, F., Drobics, M.: Extracting Knowledge and Computable Models from Data - Needs, Expectations, and Experience. In: 13th Int. Conf. on Fuzzy Systems, Budapest, Hungary. IEEE, Los Alamitos (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ramler, R., Wolfmaier, K., Stauder, E., Kossak, F., Natschläger, T. (2009). Key Questions in Building Defect Prediction Models in Practice. In: Bomarius, F., Oivo, M., Jaring, P., Abrahamsson, P. (eds) Product-Focused Software Process Improvement. PROFES 2009. Lecture Notes in Business Information Processing, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02152-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-02152-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02151-0
Online ISBN: 978-3-642-02152-7
eBook Packages: Computer ScienceComputer Science (R0)