Software Defect Localization Using Explainable Deep Learning

The rapid proliferation of software has led to an increase in security threats, causing data breaches that have severe privacy implications and substantial financial consequences. As a result, software developers are under pressure to efficiently identify and mitigate vulnerabilities. One category of tools that has gained prominence in supporting developers in this regard is the field of machine learning-based software vulnerability detection, where models are trained to classify code as either vulnerable or clean. These models offer advantages over traditional static application testing tools, including adaptability to project-specific code and tunable decision boundaries. They have shown promising performance in vulnerability discovery, outperforming traditional static analysis techniques.