An empirical benchmark of 8,408 runs across 84 tasks shows that manually designed Agent Skills improve performance by up to 16.2 percentage points, while AI‑generated skills can hurt results, and distills six actionable conclusions for crafting useful, focused, and test‑validated skills.
This study investigates the security performance of AI‑generated code in real‑world software projects, revealing high vulnerability rates, language‑specific adoption patterns, and evolving roles in the vulnerability lifecycle, and proposes a multi‑dimensional framework for risk mitigation and safe AI‑assisted development.
A recent randomized controlled study by the non‑profit AI research group METR found that, contrary to the widely held belief that AI coding tools boost developer speed by about 20%, experienced open‑source developers actually took 19% longer to complete real‑world tasks when using such tools, revealing a gap between perceived and actual productivity gains.
An empirical replication study published in the Journal of Systems and Software quantifies technical debt’s impact, showing developers waste about 23% of their work time, with additional testing, code analysis, and refactoring, and reveals a ‘contagion’ effect where existing debt prompts new debt, highlighting perception gaps between developers and managers.
This article clarifies the difference between mediation and moderation effects, explaining how each variable relationship works, illustrating with practical examples, and showing why mastering these statistical tools is essential for interpreting and designing empirical research.
A student at KIT examined thousands of GitHub repositories and discovered that code containing profanity tends to score higher on quality metrics, suggesting that expressive language may reflect deeper programmer engagement rather than simply encouraging more swearing.
This article examines the popular Java humanReadableByteCount function, reveals hidden bugs in its handling of edge cases and floating‑point precision, presents a logarithm‑based solution, discusses empirical research on Stack Overflow code reuse, and offers a robust final implementation.
