Search arXiv⌕ Search

arXiv · 2610.15567

Reviewing Beyond the Change: Repository Size and Existing Code Across Agent and Human Pull Requests

Abstract

Reviewers sometimes name existing repository items outside the change under review. We ask whether such references are more common in larger repositories, and whether the pattern differs between agent-authored and human-authored pull requests. An instrument resolves names in inline review comments against the base-commit repository; size-matched negative controls estimate chance matches. Among 92,042 comments on agent pull requests in 2,034 GitHub repositories, 6.5 per cent name an existing item outside the change. An internally pre-specified log-linear model finds an odds ratio of 1.35 per tenfold increase in code files (interval 1.15 to 1.59), and size-matched negative controls indicate that chance matching does not account for the rise. Exploratory analyses suggest that the association is concentrated from small to mid-sized repositories and is imprecise among large ones. Human pull requests in 1,845 of these repositories show a similar association. A pre-specified comparison finds a small agent excess just within the bound set as negligible, but the evidence does not establish a robust agent-specific difference. The instrument misses many references, so reported shares likely understate prevalence, and the validation cannot estimate how recall changes with repository size.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sabry E. Farrag. 2026-10-11. Reviewing Beyond the Change: Repository Size and Existing Code Across Agent and Human Pull Requests. https://arxiv.org/abs/2610.15567

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Towards a Taxonomy of Software Log Smells

Logging is an important part of modern software projects; logs are used in several tasks such as debugging and testing. Due to the complex nature of logging, it remains a difficult task with several pitfalls that could have serious consequences. Several other domains of software engineering have mitigated such threats by identifying the early signs of more serious issues, i.e., ``smells''. However, in the context of software logging, the concept of smells has only been investigated in a limited and narrow scope. The goal of this study is to systematically organize logging issues reported in the literature into a taxonomy of log smells that can help practitioners to better understand logging quality issues. To further help the practitioners and to identify issues that need more attention from the research community, we also map the identified smells to existing tools repairing them. We conducted a survey of the scientific literature to identify logging issues and related tools. After extracting relevant data from 61 articles, we used open coding to define logging issues and applied card sorting to derive log smells from these issues. Finally, we classified the tools based on their reported output. We present a taxonomy of nine log smells, describe several facets for each of them, and report the results of a preliminary validation of the taxonomy using a dataset of logging issues extracted from open-source projects. We also review existing tools repairing/removing some of these facets, highlighting the lack of tools addressing some log smells and identifying future research opportunities to close this gap. Our findings indicate that software logging is susceptible to a wide variety of log smells affecting both the logging code and the resulting log files. Understanding these smells, along with their causes and consequences, can help reduce their occurrence and improve logging quality.

cs.SE↗

From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs

[...] Since then, various APR approaches, especially those leveraging the power of large language models (LLMs), have been rapidly developed to fix general software bugs. Unfortunately, the effectiveness of these advanced techniques in the context of regression bugs remains largely unexplored. This gap motivates the need for an empirical study evaluating the effectiveness of modern APR techniques in fixing real-world regression bugs. In this work, we conduct an empirical study of APR techniques on regression bugs. To facilitate our study, we introduce RegressionBug4APR, a high-quality benchmark of Java and Python regression bugs integrated into a framework designed to facilitate APR research. The current benchmark includes 200 regression bugs collected from widely used real-world GitHub repositories. We begin by conducting an in-depth analysis of the benchmark, demonstrating its diversity and quality. Building on this foundation, we empirically evaluate the capabilities of APR to regression bugs by assessing both traditional APR tools and advanced LLM-based APR approaches. Our experimental results show that classical APR tools fail to repair any bugs, while LLM-based APR approaches exhibit promising potential. Motivated by these results, we investigate impact of incorporating bug-inducing change information into LLM-based APR approaches for fixing regression bugs. We further conduct an ablation study to disaggregate the contribution of each contextual element within the bug-inducing change information. Our results highlight that this context-aware enhancement significantly improves the performance of LLM-based APR, yielding 1.6x more successful repairs compared to using LLM-based APR without such context. Moreover, our findings are consistent across both Java and Python benchmarks, providing preliminary evidence for the generalizability of our findings.

cs.SE↗

Parameter-Efficient Multi-Task Fine-Tuning in Code-Related Tasks

Large Language Models (LLMs) have proven highly effective in automating software engineering tasks, bridging natural language and code semantics to achieve notable results in code generation and summarization. However, their scale incurs substantial computational costs, making full fine-tuning impractical. Parameter-Efficient Fine-Tuning (PEFT) methods like QLoRA enable efficient specialization with lower resource demands. Recent studies show QLoRA-optimized Large Code Models (LCMs) perform strongly across diverse tasks, yet it remains unclear whether this effectiveness persists when a single model is QLoRA fine-tuned for multiple code-related tasks. The interaction between Multi-task fine-tuning and QLoRA optimization, and how transfer learning affects correctness and quality of generated artifacts, remains largely unexplored. We investigate Multi-task QLoRA fine-tuning --- a representative parameter-efficient method from the LoRA family --- across three representative tasks: code generation, translation, and summarization. We evaluate functional correctness through execution-based and similarity-based metrics, complemented by comprehensive code quality analysis--an aspect largely overlooked in prior work. Our findings show that Multi-task QLoRA effectively leverages transfer learning, achieving competitive or superior performance at the 1.5B, 3B, and 7B configurations relative to both Single-task QLoRA and Multi-task full fine-tuning. Larger models demonstrate more consistent balance between correctness and quality, whereas smaller models preserve functionality but exhibit a higher incidence of quality-related issues.

cs.SE↗