# Understanding and Detecting Annotation-Induced Faults of Static Analyzers

arXiv: https://arxiv.org/abs/2402.14366 · HTML: https://mlsanity.com/paper/2402.14366/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [Understanding Bugs in Modern Agentic Frameworks: A Study of Symptoms, Root Causes, and Triggering Conditions](https://mlsanity.com/paper/2604.08906/) (2026)
- [Ethics Testing: Proactive Identification of Generative AI System Harms](https://mlsanity.com/paper/2604.22089/) (2026)

## Abstract (arXiv)

Static analyzers can reason about the properties and behaviors of programs and detect various issues without executing them. Hence, they should extract the necessary information to understand the analyzed program well. Annotation has been a widely used feature for different purposes in Java since the introduction of Java 5. Annotations can change program structures and convey semantics information without awareness of static analyzers, consequently leading to imprecise analysis results. This paper presents the first comprehensive study of annotation-induced faults (AIF) by analyzing 246 issues in six open-source and popular static analyzers (i.e., PMD, SpotBugs, CheckStyle, Infer, SonarQube, and Soot). We analyzed the issues' root causes, symptoms, and fix strategies and derived ten findings and some practical guidelines for detecting and repairing annotation-induced faults. Moreover, we developed an automated testing framework called AnnaTester based on three metamorphic relations originating from the findings. AnnaTester generated new tests based on the official test suites of static analyzers and unveiled 43 new faults, 20 of which have been fixed. The results confirm the value of our study and its findings.