Stratified analysis of AIDev PRs shows co-authorship effects on AI agent merge rates are artefacts of agent composition, repository selection, and PR commit structure rather than causal benefits.
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An empirical study of usages, updates and risks of third-party libraries in java projects
15 Pith papers cite this work. Polarity classification is still indexing.
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REStack is a new public dataset of 12k+ RE discussions from Stack Exchange sites, enriched with 23 LDA-derived topics grouped into six categories and community-derived difficulty metadata.
An empirical study of 547 confirmed safety incidents from GitHub and literature derives a 33-type taxonomy showing constraint violations, destructive actions, and deception dominate in everyday coding-agent use.
MultiLogBench shows that LLM performance on automated logging varies substantially across programming languages, demonstrating that single-language evidence is insufficient for general claims about model behavior or tool design.
IntentTester migrates tests across libraries using TDL abstraction and multi-agent LLM synthesis, achieving 85% correctness and 74% effectiveness versus 51% and 43% for baselines on nine projects in JSON, HTML, and Time domains.
Within-reviewer analysis of 11,429 reviews shows AI code approval rising from 30.1% to 36.8% with experience, with reduced inline comments and increased latency, consistent with habituation.
SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
APIKG4Syn synthesizes API-oriented training data via knowledge graphs and Monte Carlo search to fine-tune a 7B model that reaches 25% pass@1 on HarmonyOS code generation, beating untuned GPT-4o at 17.59%.
MR-Adopt deduces input transformations from hard-coded MR test cases using LLMs, data-flow refinement, and output-relation selection to enable reuse with new source inputs.
MR-Scout extracts over 11,000 metamorphic-relation-encoded test cases from 701 OSS projects, codifies 97% of them as high-quality generators, and shows they raise line coverage by 13.52% and mutation score by 9.42% on programs that already have developer tests.
Analysis of 252 bug fixes in an LLM-powered multi-market web app found 44% escaped through four seams invisible to component unit tests, motivating a four-seam verification framework.
Developers most frequently reference the full Log4j migration guide in pull request descriptions (82.81% of cases) and continue consulting it during post-update maintenance tasks.
Empirical review of 233 real-world vulnerabilities from 34 TON audits produces a specialized checklist for asynchronous message handling, supported by case studies and an 11-person practitioner survey.
Generative AI suitability in qualitative research depends primarily on the approach (small-q positivist/post-positivist or Big Q non-positivist) along with skills, ethics, and personal preferences.