## Mind the Gap: Do Widely-used SAST Tools Really Cover the Language-Specific Top CWEs? This repository contains the data, evaluation criteria, and experimental results for the research paper: "Mind the Gap: Do Widely-used SAST Tools Really Cover theLanguage-Specific Top CWEs?". This study presents a large-scale empirical analysis of nine popular Static Application Security Testing (SAST) tools to evaluate their coverage of language-specific top CWEs across C/C++, Java, and Python. Project Overview Static Application Security Testing (SAST) tools are critical for identifying vulnerabilities early, but existing evaluations often focus on language-agnostic rankings like the MITRE CWE Top 25. This project addresses the mismatch between tool capabilities and developers' actual security needs by:Constructing Language-Specific Datasets: Identifying top CWEs from 197,016 CVEs reported over the past decade. Evaluating Coverage Gaps: Analyzing 9 popular SAST tools to identify where they fail to detect common vulnerabilities. Assessing Migration Potential: Investigating how multi-language tools can reuse existing checkers to fill coverage gaps. ## Repository Structure The project is organized into the following core components: CVEData/ Contains the raw and processed vulnerability data used to identify top CWEs.Primary Source: 197,016 labeled CVE entries from the National Vulnerability Database (NVD) covering 2016 to 2025.Heuristic Labeling: Implementation of the automated labeling process—using file extensions, language names, and 167 manually selected keywords—to map CVEs to specific languages.Accuracy: Manual validation confirms an overall labeling accuracy of 91.5%. CWEView1000/ Contains the hierarchical data for CWE View 1000 (Research Concepts).Comprehensive Set: Includes 944 CWE types organized by their underlying causes and patterns. Root Categories: Systematic organization of all CWEs into 10 root categories (e.g., CWE-664, CWE-707, CWE-284) to ensure a holistic evaluation. ToolData/ Technical specifications and checker data for the nine evaluated SAST tools.Analyzed Tools: CodeQL, Semgrep, SonarQube, Cppcheck, Clang Static Analyzer (CSA), Flawfinder, Insider, SpotBugs + FindSecBugs, and Bandit. Tool Metadata: Includes descriptions of analysis paradigms (e.g., Datalog-based semantic engines, symbolic execution, pattern matching) and the specific CWE checkers each tool implements. RQ Results.xlsx Detailed results for the three core Research Questions (RQs) ## Key Findings Language Sensitivity: Vulnerability patterns are highly language-dependent; Jaccard similarity between C/C++ and other languages is only 0.17 to 0.18.C/C++ Priority: SAST coverage is lowest for C/C++, highlighting a critical need for tool improvement in this area. Complementary Strengths: No single tool achieves complete coverage; practitioners should combine tools based on their specific detection goals. Migration Potential: Leveraging checker migration between languages is a cost-effective way to extend CWE coverage, especially for multi-language tools.