Abstract
This chapter is a survey of close to ten years of software fault prediction research performed by our group. We describe our initial motivation, the variables used to make predictions, provide a description of our standard model based on Negative Binomial Regression, and summarize the results of using this model to make predictions for nine large industrial software systems. The systems range in size from hundreds of thousands to millions of lines of code. All have been in the field for multiple years and many releases, and continue to be maintained and enhanced, usually at 3 month intervals.
Effectiveness of the fault predictions is assessed using two different metrics. We compare the effectiveness of the standard model to augmented models that include variables related to developer counts, to inter-file calling structure, and to information about specific developers who modified the code.
We also evaluate alternate prediction models based on different training algorithms, including Recursive Partitioning, Bayesian Additive Regression Trees, and Random Forests.
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
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)
Chipman, H.A., George, E.I., McCulloch, R.E.: BART: Bayesian Additive Regression Trees (2008), http://arxiv.org/abs/0806.3286v1
McCullagh, P., Nelder, J.A.: Generalized Linear Models, 2nd edn. Chapman and Hall, London (1989)
Ostrand, T.J., Weyuker, E.J.: The Distribution of Faults in a Large Industrial Software System. In: International Symposium on Software Testing and Analysis (ISSTA 2002), pp. 55–64. ACM Press, New York (2002)
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)
Ostrand, T.J., Weyuker, E.J., Bell, R.M.: Programmer-based Fault Prediction. In: Predictive Models for Software Engineering (PROMISE 2010). ACM Press, New York (2010)
Shin, Y., Bell, R.M., Ostrand, T.J., Weyuker, E.J.: On the use of calling structure information to improve fault prediction. Empirical Software Eng. (July 2011), http://www.springerlink.com/content/r4q76v4317148451/
Weyuker, E.J., Ostrand, T.J., Bell, R.M.: We’re Finding Most of the Bugs, but What are we Missing? In: 3rd International Conference on Software Testing. IEEE Press, New York (2010)
Weyuker, E.J., Ostrand, T.J., Bell, R.M.: Do Too Many Cooks Spoil the Broth? Using the Number of Developers to Enhance Defect Prediction Models. Empirical Software Eng. 13(5), 539–559 (2008)
Weyuker, E.J., Ostrand, T.J., Bell, R.M.: Comparing the Effectiveness of Several Modeling Methods for Fault Prediction. Empirical Software Eng. 15(3), 277–295 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ostrand, T.J., Weyuker, E.J. (2013). Predicting Bugs in Large Industrial Software Systems. In: De Lucia, A., Ferrucci, F. (eds) Software Engineering. ISSSE ISSSE ISSSE 2010 2009 2011. Lecture Notes in Computer Science, vol 7171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36054-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-36054-1_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36053-4
Online ISBN: 978-3-642-36054-1
eBook Packages: Computer ScienceComputer Science (R0)