Software security faces a persistent gap: static analysis tools detect vulnerabilities effectively, but their technical outputs remain inaccessible to most developers. This leads to mounting security debt, as organizations must rely on security specialists for remediation, creating bottlenecks that delay fixes. This paper proposes an interpretability convention and a modular workflow that transforms raw static analyzer outputs into clear, actionable vulnerability reports for all developers, not just security experts. Our tool, SECGen, automates the workflow by parsing static analyzer outputs and restructuring them into clear, developer-friendly reports based on our convention, and enforcing compliance through automated validation. We validated our approach through a user study with 25 developers, comparing our interpretable reports to other state-of-the-art static analyzer outputs. The results suggest that developers using interpretable reports detect, understand and fix vulnerabilities more effectively, requiring only 67% of the time typically spent with traditional reports while writing more correct fixes. Key reasons for this include participants’ preference for structured reports, with clear vulnerability descriptions and actionable fix suggestions.

Automatic vulnerability detection is of paramount importance to promote the security of an application and should be exercised at the earliest stages within the software development life cycle (SDLC) to reduce the risk of exposure. Despite the advancements with state-of-the-art deep learning techniques in software vulnerability detection, the development environments are not yet leveraging their performance. In this work, we integrate the Transformers architecture, one of the main highlights of advances in deep learning for Natural Language Processing, within a developer-friendly tool for code security. We introduce VDet for Java, a transformer-based VS Code extension that enables one to discover vulnerabilities in Java files. Our preliminary model evaluation presents an accuracy of 98.9% for multi-label classification and can detect up to 21 vulnerability types. The demonstration of our tool can be found at https://youtu.be/OjiUBQ6TdqE, and source code and datasets are available at https://github.com/TQRG/VDET-for-Java.

Deep learning (DL) techniques have demonstrated potential in reasoning complex patterns of vulnerable code from high-level abstractions. Recent advancements in the area, such as the introduction of transformer-based models, like BERT, help overcome the problem of the available vulnerability detection datasets being too small to enable most DL models to capture all relevant patterns. They mitigate the challenge by leveraging knowledge from a general domain to solve problems in specific domains. In this paper, we explore different BERT-based models for multi-label classification of vulnerabilities in Java on a synthetic dataset. The models yield up to 99% in accuracy and 94% in f1-score. We remove biases in the training dataset and observe drops of up to 13% of the f1-score. We further assess the generalizability of the models on realistic samples and notice that one model, in particular, predicted unknown vulnerabilities with an f1-score of nearly 85%.