This paper presents a novel end-to-end approach to program repair based on sequence-to-
sequence learning. We devise, implement, and evaluate a technique, called SequenceR, for …

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

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

Despite the capability in successfully fixing more and more real-world bugs, existing
Automated Program Repair (APR) techniques are still challenged by the long-standing …

A large body of research efforts have been dedicated to automated software debugging,
including both automated fault localization and program repair. However, existing fault …

Large Language Models (LLMs) have shown promise in multiple software engineering tasks
including code generation, code summarisation, test generation and code repair. Fault …

Large Language Models (LLMs) have shown a surprising level of performance on multiple
software engineering problems. However, they have not yet been applied to fault …

In software debugging, fault localization is the most difficult, expensive, tedious, and time-
consuming task, particularly for large-scale software systems. This is due to the fact that it …

Inferring program transformations from concrete program changes has many potential uses,
such as applying systematic program edits, refactoring, and automated program repair …

As Deep Learning (DL) systems are widely deployed for mission-critical applications,
debugging such systems becomes essential. Most existing works identify and repair …