Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …

The proliferation of services and service interactions within microservices and cloud-native
applications, makes it harder to detect failures and to identify their possible root causes …

Automated Program Repair (APR) aims to automatically generate patches for buggy
programs. Recent APR work has been focused on leveraging modern Large Language …

Researchers have made significant progress in automating the software development
process in the past decades. Automated techniques for issue summarization, bug …

Due to the promising future of Automated Program Repair (APR), researchers have
proposed various APR techniques, including heuristic-based, template-based, and …

Recent advancements in large language models (LLMs) have significantly advanced the
automation of software development tasks, including code synthesis, program repair, and …

Automated program repair (APR) aims to fix software bugs automatically and plays a crucial
role in software development and maintenance. With the recent advances in deep learning …

Tests that fail inconsistently, without changes to the code under test, are described as flaky.
Flaky tests do not give a clear indication of the presence of software bugs and thus limit the …

We revisit the performance of template-based APR to build comprehensive knowledge
about the effectiveness of fix patterns, and to highlight the importance of complementary …

The complexity and dynamism of microservice systems pose unique challenges to a variety
of software engineering tasks such as fault analysis and debugging. In spite of the …