Skip to content

Investigating Developer-Reported Software Security Testing Challenges

Sep 2026 · 0 citations · 71 references
Computer Science

TL;DR

Overall, developer-reported SST challenges extend beyond vulnerability detection and include workflow, configuration, interpretation, and remediation concerns and can help researchers, practitioners, educators, and tool providers improve SST usability, documentation, result interpretation, and remediation support.

Abstract

Software security testing (SST) is essential for identifying vulnerabilities and improving software security, but developers often face practical challenges when selecting tools, configuring test environments, interpreting scanner outputs, testing authentication workflows, and acting on reported vulnerabilities. This study empirically characterizes developer-reported SST challenges in Stack Overflow (SO) discussions and examines their prevalence, difficulty, temporal evolution, co-occurrence, and taxonomy stability. We analyze 17,743 SO questions collected using SST-related keywords. Through manual labeling, we identify 582 SST-related questions and construct a taxonomy of 8 categories and 31 subcategories. We then analyze these challenges using prevalence, difficulty, correlation, temporal trend, co-occurrence, and held-out stability analyses. The results show that developers frequently discuss security finding interpretation and reliability, security testing guidance and tool selection, authentication and authorization testing, and security tool integration and automation. Validation-related challenges often require more technical context and longer resolution time. Security finding interpretation and remediation-related actionability show increasing trends over time. Co-occurrence analysis shows that false positives frequently appear with explainability, while integration challenges often appear with tool suggestions and documentation/resources. Overall, developer-reported SST challenges extend beyond vulnerability detection and include workflow, configuration, interpretation, and remediation concerns. These findings can help researchers, practitioners, educators, and tool providers improve SST usability, documentation, result interpretation, and remediation support.

View source

Similar papers

#software testing Review Open access Sep 2026

DevSecOps: A Comprehensive Survey of Secure Software Development and Deployment Practices

DevSecOps brings together development, security, and operations approaches to incorporate security across the software development lifecycle, allowing organizations to discover and address vulnerabilities earlier while ensuring quick and dependable software delivery. This survey reviews the foundations, practices, tech...

Prashant Kumar Shrivastava · 0 citations
#software testing Open access Aug 2026

HawkEye: Web Vulnerability Analysis and Security Audit Tool

HawkEye is introduced, a modular, web-based vulnerability auditing platform designed to streamline security analysis by integrating multiple scanning tools within a unified dashboard and illustrates how consolidated reporting improves vulnerability prioritization for development teams.

D. R. Patil, Varad Salgare, Devaj Arya et al. · 0 citations
Review Sep 2026

An Empirical Analysis of CodeQL False Positives and Query Refinements for Java Vulnerabilities

Static application security testing (SAST) tools help developers find vulnerabilities before deployment, but false positives create substantial triage effort. We study whether CodeQL false positives in Java security analysis form recurring, explainable patterns that can be reduced by refining the analysis. We run CodeQ...

Amirali Sajadi, Saikat Dutta, Preetha Chatterjee · 0 citations
Review Sep 2026

Understanding the Usability of Cryptographic Verification Tools

Cryptographic protocol verification tools are widely used to analyze the security of complex protocols, yet how users interact with these tools remains comparatively understudied. We present an exploratory human-centered study of experienced users of Tamarin, ProVerif, and related protocol verifiers. Our survey include...

Tarikul Islam, Y. Islam, Khandakar Ashrafi Akbar et al. · 0 citations
Review Aug 2026

Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda

This evidence-centered structured survey synthesizes representative work available through May 31, 2026 across software engineering tasks, software security tasks, adaptation mechanisms, artifact granularity, and evaluation design and introduces an assurance framework that separates functional correctness, security, op...

Wei Lin, Tao Zhou, Zhaofei Xie et al. · 0 citations
Open access Aug 2026

Static Code Analysis Framework for Automated Security Vulnerability Detection

Experimental results show that AST-based structural features substantially improve recall compared with the TF-IDF baseline, while the combined TF-IDF and AST representation maintains this improved performance.

Vani Pasupula, M. N. V. Manikanth, Nagaraju Vassey · 0 citations

Related blog posts

MIT News · Artificial Intelligence Oct 2, 2026

Documenting the tech worker movement

Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.

GPT-Lab Sep 23, 2026

Requirements Don’t Live in Isolation: What We’re Exploring with Req-Space

Requirements in large systems rarely exist in isolation. Their meaning depends on the wider project context - other requirements, policies, decisions, tests, and implementation details. That becomes especially important when AI is used for review, because spotting a possible conflict or gap is only the beginning. ReqSpace explores how AI, visualisation, and connected project context can help reviewers understand those findings, trace the relationships behind them, and focus on the questions that…

GPT-Lab Sep 17, 2026

Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering

AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.