An empirical assessment of machine learning approaches for triaging reports of static analysis tools

Despite their ability to detect critical bugs in software, static analysis tools' high false positive rates are a key barrier to their adoption in real-world settings. To improve the usability of these tools, researchers have recently begun to apply machine learning techniques to classify and filter incorrect analysis reports. Although initial results have been promising, the long-term potential and best practices for this line of research are unclear due to the lack of detailed, large-scale empirical evaluation. To partially address this knowledge gap, we present a comparative empirical study of three machine learning techniques & mdash;traditional models, recurrent neural networks (RNNs), and graph neural networks (GNNs)& mdash;for classifying correct and incorrect results in three static analysis tools & mdash;FindSecBugs, CBMC, and JBMC & mdash;using multiple datasets. These tools represent different techniques of static analysis, namely taint analysis and model-checking. We also introduce and evaluate new data preparation routines for RNNs and node representations for GNNs. We find that overall classification accuracy reaches a high of 80%-99% for different datasets and application scenarios. We observe that data preparation routines have a positive impact on classification accuracy, with an improvement of up to 5% for RNNs and 16% for GNNs. Overall, our results suggest that neural networks (RNNs or GNNs) that learn over a program's source code outperform traditional models, although interesting tradeoffs are present among all techniques. Our observations provide insight into the future research needed to speed the adoption of machine learning approaches for static analysis tools in practice.