Static Code Analysis (SCA) tools play a vital role in software development, reducing the cost and time required for code reviews. However, high false-positive and false-negative rates are reported for the best tools in the community. Accordingly, studies often aim to develop datasets for learning SCA warning patterns to reduce false positive results. These datasets are meant to possess high-quality and high-volume in covering the full range of faults/rules that typically result in false warnings and be compliant with established coding standards. However, existing studies have not utilised such datasets or identified the breadth of rules that are prone to false-positives and their compliance to coding standards. We analysed code from Stack Overflow and Apache Tomcat to capture variations in code length and style in detecting false-positive warnings from best-performing tools PMD and SonarQube, addressing this gap. In deriving false-positive warnings, outcomes from the tools were labelled using established coding standards. Deeper analyses were then conducted to identify the rules that are prone to false-positives, reasons for these, and agreement/gaps between SCA rules and established standards. Among our main outcomes, we observe that only a few SCA rules generate false-positives, ranging from 4.64% to 18.45% across four datasets. Additionally, eliminating rules that contradict established standards significantly reduce the false-positive rate. Additionally, our findings reveal discrepancies between tools and established standards. Given the evidence established in this study, we recommend further investigations into gaps between tools and established standards, including the use of machine learning approaches to annotate larger datasets.
Deshapriya et al. (Sun,) studied this question.