Software bug

Oceanography

Machine learning

Data mining

Baseline (sea)

Geology

Software system

Software construction

Software regression

Programming language

Source lines of code

Software

Computer science

Software vulnerabilities are prevalent in software systems, causing a variety of problems including deadlock, information loss, or system failures. Thus, early predictions of software vulnerabilities are critically important in safety-critical software systems. Various ML/DL-based approaches have been proposed to predict vulnerabilities at the file/function/method level. Recently, IVDetect (a graph-based neural network) is proposed to predict vulnerabilities at the function level. Yet, the IVDetect approach is still inaccurate and coarse-grained. In this paper, we propose LineVul, a Transformer-based line-level vulnerability prediction approach in order to address several limitations of the state-of-the-art IVDetect approach. Through an empirical evaluation of a large-scale real-world dataset with 188k+ C/C++ functions, we show that LineVul achieves (1) 160%-379% higher F1-measure for function-level predictions; (2) 12%-25% higher Top-10 Accuracy for line-level predictions; and (3) 29%-53% less [email protected]%Recall than the baseline approaches, highlighting the significant advancement of LineVul towards more accurate and more cost-effective line-level vulnerability predictions. Our additional analysis also shows that our LineVul is also very accurate (75%-100%) for predicting vulnerable functions affected by the Top-25 most dangerous CWEs, highlighting the potential impact of our LineVul in real-world usage scenarios.

LineVul