Despite being one of the most basic tasks in software development, debugging is still
performed in a mostly manual way, leading to high cost and low performance. To address …

Mutation testing realizes the idea of using artificial defects to support testing activities.
Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the …

Learning-based fault localization has been intensively studied recently. Prior studies have
shown that traditional Learning-to-Rank techniques can help precisely diagnose fault …

Most fault localization techniques take as input a faulty program, and produce as output a
ranked list of suspicious code locations at which the program may be defective. When …

Automatically detecting software vulnerabilities is an important problem that has attracted
much attention from the academic research community. However, existing vulnerability …

The performance of fault localization techniques is critical to their adoption in practice. This
paper reports on an empirical study of a wide range of fault localization techniques on real …

In this paper, we propose DeepRL4FL, a deep learning fault localization (FL) approach that
locates the buggy code at the statement and method levels by treating FL as an image …

Coverage-based fault localization has been extensively studied in the literature due to its
effectiveness and lightweightness for real-world systems. However, existing techniques …

Automated Program Repair (APR) is one of the most recent advances in automated
debugging, and can directly fix buggy programs with minimal human intervention. Although …

Fault Localization (FL) aims to automatically localize buggy lines of code, a key first step in
many manual and automatic debugging tasks. Previous FL techniques assume the provision …