Abstract Information retrieval fault localization (IRFL) is a popular research field and many
IRFL approaches have been proposed recently. Unfortunately, the evaluation of some of …

In educational settings, automated program repair techniques serve as a feedback
mechanism to guide students working on their programming assignments. Recent work has …

Deep learning (DL) techniques have demonstrated potential in reasoning complex patterns
of vulnerable code from high-level abstractions. Recent advancements in the area, such as …

The MAP (Mean Average Precision) metric is one of the most popular performance metrics
in the field of Information Retrieval Fault Localization (IRFL). However, there are problematic …

We assess the reproducibility of the BugsInPy dataset less than three years after its original
publication. The bug dataset provides some information about the software environment in …

Fault localization, aiming at localizing the root cause of the bug under repair, has been a
longstanding research topic. Although many approaches have been proposed in past …

Over the years, security researchers have developed a broad spectrum of automatic code
scanners that aim to find security vulnerabilities in applications. Security benchmarks are …

The point has repeatedly been made that validation is a crucial success factor in
demonstrating the scientific contribution and ensuring the adoption of results. Still …

Security code smells are receiving increasing attention in the domain of Android app
development. They serve as coding guidelines aimed at identifying vulnerabilities …

Context. Software is subject to the presence of faults, which impacts its quality as well as
production and maintenance costs. Evolutionary fault localization uses data from the test …