Towards Better Static Code Analysis Reports: Sentence Transformer-based Filtering of Non-Actionable Alerts

2026-04-20

AI Summary

Static code analysis tools often suffer from generating excessive non-actionable warnings, leading to developer alert fatigue and reduced practical utility. This work proposes STAF, a novel approach that, for the first time, leverages Sentence Transformers to classify the actionability of static analysis alerts. By employing sentence embeddings within a binary classification framework, STAF is trained and evaluated on a large-scale dataset of Java projects. Experimental results demonstrate that STAF achieves an F1 score of 89% in within-project settings, outperforming existing methods by at least 11%. In cross-project scenarios, it improves performance by at least 6%, substantially reducing non-actionable alerts and significantly enhancing the overall quality of analysis reports.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Evaluation and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Static code analysis (SCA) tools are widely used as effective ways to detect bugs and vulnerabilities in software systems. However, the reports generated by these tools often contain a large number of non-actionable findings, which can overwhelm developers to the point of ignoring them altogether -- this phenomenon is known as "alert fatigue". In this paper, we combat alert fatigue by proposing STAF: Sentence Transformer-based Actionability Filtering. Our approach leverages a transformer based architecture with sentence embeddings to classify findings into actionable and non-actionable categories. Evaluating STAF on a large dataset of reports from Java projects, we demonstrate that our method can effectively reduce the number of non-actionable findings while maintaining a high level of accuracy in identifying actionable issues. The results show that our approach can improve the usability of static analysis tools reaching an F1 score of 89%, outperforming existing methods for SCA warning filtering by at least 11% in a within-project setting and by at least 6% in a cross-project setting. By providing a more focused and relevant set of findings, we aim to enhance the overall effectiveness of static analysis in software development.

Problem

• static code analysis
• alert fatigue
• non-actionable alerts
• code quality
• software maintenance

Innovation

• Sentence Transformer
• Static Code Analysis
• Alert Filtering
• Actionability Classification
• Alert Fatigue