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Localizing Bugs in Program Executions with Graphical Models

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44319

Dietz,  Laura
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45377

Scheffer,  Tobias
Machine Learning, MPI for Informatics, Max Planck Society;

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Zitation

Dietz, L., Dallmeier, V., Zeller, A., & Scheffer, T. (2009). Localizing Bugs in Program Executions with Graphical Models. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22 (NIPS09) (pp. 468-477). La Jolla, CA: NIPS Foundation. Retrieved from http://books.nips.cc/papers/files/nips22/NIPS2009_0704.pdf.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-192A-7
Zusammenfassung
We devise a graphical model that supports the process of debugging software by guiding developers to code that is likely to contain defects. The model is trained using execution traces of passing test runs; it reflects the distribution over transitional patterns of code positions. Given a failing test case, the model determines the least likely transitional pattern in the execution trace. The model is designed such that Bayesian inference has a closed-form solution. We evaluate the Bernoulli graph model on data of the software projects AspectJ and Rhino.