Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Information Needs in Collocated Software Development Teams
ICSE '07 Proceedings of the 29th international conference on Software Engineering
A Systematic Review of Theory Use in Software Engineering Experiments
IEEE Transactions on Software Engineering
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
What do large commits tell us?: a taxonomical study of large commits
Proceedings of the 2008 international working conference on Mining software repositories
The impact of process choice in high maturity environments: An empirical analysis
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Developer fluency: achieving true mastery in software projects
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Software analytics as a learning case in practice: approaches and experiences
Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering
Defect, defect, defect: defect prediction 2.0
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Assessing the value of branches with what-if analysis
Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
How not to survey developers and repositories: experiences analyzing language adoption
Proceedings of the ACM 4th annual workshop on Evaluation and usability of programming languages and tools
Predicting method crashes with bytecode operations
Proceedings of the 6th India Software Engineering Conference
Journal of Systems and Software
The eclipse and mozilla defect tracking dataset: a genuine dataset for mining bug information
Proceedings of the 10th Working Conference on Mining Software Repositories
DConfusion: a technique to allow cross study performance evaluation of fault prediction studies
Automated Software Engineering
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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.