Journal:
Information and Software Technology, Volume 19, January 2026
Authors:
Kalouptsoglou I, Siavvas M, Ampatzoglou A, Kehagias D, Chatzigeorgiou A.
Abstract:
Context: The development of secure software systems depends on early and accurate vulnerability identification. Manual inspection is a time-consuming process that requires specialized knowledge. Therefore, as software complexity grows, automated solutions become essential. Vulnerability Prediction (VP) is an emerging mechanism that identifies whether software components contain vulnerabilities, commonly using Machine Learning models trained on classifying components as vulnerable or clean. Recent explainability-based approaches attempt to rank the lines based on their influence on the output of the VP Models (VPMs). However, challenges remain in accurately localizing the vulnerable lines.
Objective: This study aims to examine an alternative to explainability-based approaches to overcome their shortcomings. Specifically, explainability-based methods depend on the type and accuracy of the file or function-level VPMs, inherit possible misleading patterns, and cannot indicate the exact code snippet that is vulnerable nor the number of vulnerable lines.
Method: To address these limitations, this study introduces an innovative approach based on fine-tuning Large Language Models on a Sequence-to-Sequence objective to directly return the vulnerable lines of a given function. The method is evaluated on the Big-Vul dataset to assess its capacity for fine-grained vulnerability detection.
Results: The results demonstrate that the proposed method significantly outperforms the explainability-based baseline both in terms of accuracy and cost-effectiveness.
Conclusions: The proposed approach marks a significant advancement in automated vulnerability detection by enabling accurate line-level localization of vulnerabilities.
