Predicting accurate and actionable static analysis warnings

Static analysis tools report software defects that may or may not be detected by other verification methods. Two challenges complicating the adoption of these tools are spurious false positive warnings and legitimate warnings that are not acted on. This paper reports automated support to help address these challenges using logistic regression models that predict the foregoing types of warnings from signals in the warnings and implicated code. Because examining many potential signaling factors in large software development settings can be expensive, we use a screening methodology to quickly discard factors with low predictive power and cost-effectively build predictive models. Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative models built without screening.

1. INTRODUCTION

Static analysis tools detect software defects by analyzing a system without actually executing it. These tools utilize information from fixed program representations such as source code, generated or compiled code, and abstractions or models of the system. Even relatively simple analyses, such as detecting pointer dereferences after null checks, can find many defects in real software [5], [9].