Static analysis (SA) tools can generate useful static warnings to reveal the problematic code
snippets in a software system without dynamically executing the corresponding source code …

Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static
code analyzers. With recent advances in Machine Learning (ML), various approaches have …

Static analysis tools for capturing bugs and vulnerabilities in software programs are widely
employed in practice, as they have the unique advantages of high coverage and …

Actionable warning identification (AWI) is crucial for improving the usability of static analysis
tools. Currently, machine learning (ML)-based AWI approaches are notably common, which …

Software vulnerabilities resulting from coding weaknesses and poor development practices
are common. Attackers can exploit these vulnerabilities and impact the security and privacy …

Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static
code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainly …

Static analysis tools generate a large number of alarms that require manual inspection. In
prior work, repositioning of alarms is proposed to (1) merge multiple similar alarms together …

Excessive non-actionable warnings generated by static program analysis tools can hinder
developers from utilizing these tools effectively. Leveraging learning-based approaches for …

Automatic static analysis tools (ASATs), also known as static analyzers, have demonstrated
their significance and practicability in detecting software defects. ASATs assist developers to …

Static analysis techniques enhance the security, performance, and reliability of programs by
analyzing and portraiting program behaviors without the need for actual execution. In …