GPU clusters underpin modern deep learning, yet studies across industry and academia consistently report widespread GPU underutilization. Prior work and our own analysis indicate that inefficiency often stems from recurring patterns in code, job scripts, and runtime behaviour that users rarely detect. We argue that addressing this issue is a MSR challenge: it requires mining inefficiency patterns, combining static and dynamic signals for actionable feedback, validating job-submission artefacts, and developing privacy-aware datasets linking code, configuration, and runtime metrics.

The modern software development landscape heavily relies on transitive dependencies. They enable seamless integration of third-party libraries. However, they also introduce security challenges. Transitive vulnerabilities that arise from indirect dependencies expose projects to risks associated with Common Vulnerabilities and Exposures (CVEs). It happens even when direct dependencies remain secure. This paper examines the lifecycle of transitive vulnerabilities in the Maven ecosystem. We employ survival analysis to measure the time projects remain exposed after a CVE is introduced. Using a large dataset of Maven projects, we identify factors that influence the resolution of these vulnerabilities. Our findings offer practical advice on improving dependency management.

We present HaPy-Bug, a curated dataset of 793 Python source code commits associated with bug fixes, with each line of code annotated by three domain experts. The annotations offer insights into the purpose of modified files, changes at the line level, and reviewers’ confidence levels. We analyze HaPy-Bug to examine the distribution of file purposes, types of modifications, and tangled changes. Additionally, we explore its potential applications in bug tracking, the analysis of bug-fixing practices, and the development of repository analysis tools. HaPy-Bug serves as a valuable resource for advancing research in software maintenance and security.

In the dynamic landscape of software engineering, the emergence of ChatGPT-generated code signifies a distinctive and evolving paradigm in development practices. We delve into the impact of interactions with ChatGPT on the software development process, specifically analysing its influence on source code changes. Our emphasis lies in aligning code with ChatGPT conversations, separately analysing the user-provided context of the code and the extent to which the resulting code has been influenced by ChatGPT. Additionally, employing survival analysis techniques, we examine the longevity of ChatGPT-generated code segments in comparison to lines written traditionally. The goal is to provide valuable insights into the transformative role of ChatGPT in software development, illuminating its implications for code evolution and sustainability within the ecosystem.

The Common Vulnerabilities and Exposures (CVEs) system is a reference method for documenting publicly known information security weaknesses and exposures. This paper presents a study of the lifetime of CVEs in software projects and the risk factors affecting their existence. The study uses survival analysis to examine how features of programming languages, projects, and CVEs themselves impact the lifetime of CVEs. We suggest avenues for future research to investigate the effect of various factors on the resolution of vulnerabilities.

We demonstrate a new idea of a parallel GPU JSON parser, which is able to optimize the parsing and initial transformation process through metaprogramming. It outperforms other well-known solutions like simdjson, Pandas, as well as cuDF– which also works on GPU. The resulting data is ready to be further processed in common data frame formats and may be incorporated by RAPIDS, Apache Arrow or Pandas. Our parser can therefore be a part of an industrial Extract-Transform-Load workflow.