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 associate 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.