Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2009 | NIPS | Programming language,Bayesian inference,Computer science,Theoretical computer science,Software,AspectJ,Graphical model,Graph model,Bernoulli's principle,Debugging |
DocType | Citations | PageRank |
Conference | 8 | 0.57 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura Dietz | 1 | 339 | 28.86 |
| Valentin Dallmeier | 2 | 539 | 25.99 |
| Andreas Zeller | 3 | 5697 | 303.71 |
| Tobias Scheffer | 4 | 1862 | 139.64 |