| Title | Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Koc, Ugur, Saadatpanah, Parsa, Foster, Jeffrey S., Porter, Adam A. |
| Conference Name | Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5071-6 |
| Keywords | composability, Human Behavior, long short-term memories, naïve Bayes classifier, Program slicing, pubcrawl, resilience, Resiliency, static code analysis |
| Abstract | The large scale and high complexity of modern software systems make perfectly precise static code analysis (SCA) infeasible. Therefore SCA tools often over-approximate, so not to miss any real problems. This, however, comes at the expense of raising false alarms, which, in practice, reduces the usability of these tools. To partially address this problem, we propose a novel learning process whose goal is to discover program structures that cause a given SCA tool to emit false error reports, and then to use this information to predict whether a new error report is likely to be a false positive as well. To do this, we first preprocess code to isolate the locations that are related to the error report. Then, we apply machine learning techniques to the preprocessed code to discover correlations and to learn a classifier. We evaluated this approach in an initial case study of a widely-used SCA tool for Java. Our results showed that for our dataset we could accurately classify a large majority of false positive error reports. Moreover, we identified some common coding patterns that led to false positive errors. We believe that SCA developers may be able to redesign their methods to address these patterns and reduce false positive error reports. |
| URL | http://doi.acm.org/10.1145/3088525.3088675 |
| DOI | 10.1145/3088525.3088675 |
| Citation Key | koc_learning_2017 |
Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools