Andreas Zeller, Thomas Zimmermann, and Christian Bird
Background: The past years have seen a surge of techniques predicting failure-prone locations based on more or less complex metrics. Few of these metrics are actionable, though. Aims: This paper explores a simple, easy-to-implement method to predict and avoid failures in software systems. The IROP method links elementary source code features to known software failures in a lightweight, easy-to-implement fashion. Method: We sampled the Eclipse data set mapping defects to files in three Eclipse releases. We used logistic regression to as- sociate programmer actions with defects, tested the predictive power of the resulting classifier in terms of precision and recall, and isolated the most defect-prone actions. We also collected initial feedback on possible remedies. Results: In our sample set, IROP correctly predicted up to 74% of the failure-prone modules, which is on par with the most elaborate predictors available. We isolated a set of four easy-to-remember recommendations, telling programmers precisely what to do to avoid errors. Initial feedback from developers suggests that these recommendations are straightforward to follow in practice. Conclusions: With the abundance of software development data, even the simplest methods can produce “actionable” results.
|Published in||Proceedings of the 7th International Conference on Predictive Models in Software Engineering|