Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.32057

CG-Diff: Organizing Code Changes Around Call Graphs

Bimal Raj Gyawali·Devamardeep Hayatpur·Nadia Polikarpova
View original source ↗Read source document ↗Export metadata ↓

Abstract

Tools for code review present code changes file-by-file. We argue that, oftentimes, changes can be better organized around a call graph of the changed code. From an empirical study of GitHub pull requests (PRs) in-the-wild, we find that (1) in around 40% of the PRs, more than half of the changed functions (and methods) are connected by call graphs, and (2) necessary callees are often located in different files from their callers. Based on these findings, we develop the notion of CG-Diffs, subgraphs derived by decomposing a call graph of the changed code into smaller and more navigable directed graphs. We then implement a web-based interface for viewing PRs that restructures the PR around these CG-Diffs. Through a within-subject study comparing our interface against GitHub's PR view, we find that CG-Diffs help orient participants and provide them with more meaningful structures to navigate the PR. We also found several limitations: function nodes repeated across multiple CG-Diffs can be disorienting, and changes not contained in a function (e.g. globals, imports) are not as immediately apparent. Our study shows promise in using call graphs to help contextualize and navigate unfamiliar codebases, which may benefit new contributors to open source, and reviewers of unfamiliar LLM-generated PRs.

Explore related subjects

cs.SEcs.HC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bimal Raj Gyawali, Devamardeep Hayatpur, Nadia Polikarpova. 2026-09-25. CG-Diff: Organizing Code Changes Around Call Graphs. https://arxiv.org/abs/2609.32057

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

KEEP EXPLORING

Related papers

arXiv2026

Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?

A widespread practice in software development is to tailor coding agents to repositories using context files, such as AGENTS.md. Although this practice is strongly encouraged by agent developers, there is currently no rigorous investigation into whether such context files are actually effective for real-world tasks. In this work, we study this question and evaluate coding agents' task completion performance in two complementary settings: established SWE-bench tasks from popular repositories, with LLM-generated context files, and a novel collection of issues from repositories containing developer-committed context files. Surprisingly, we find that providing context files does not generally improve task success rates, while increasing inference cost by over 20% on average. This observation holds across different LLMs, coding agents, and for both LLM-generated and developer-committed context files. Specifically, we find that while instructions in the context files are well followed by coding agents, repository overviews, although popular and recommended by model providers, are not helpful. We conclude that while context files are useful for specifying non-standard coding practices, any attempts to improve performance should be rigorously evaluated before deployment.

cs.SE↗
arXiv2026

PlayCoder: Making LLM-Generated GUI Code Playable

Large language models (LLMs) have achieved strong results in code generation, but their ability to generate GUI applications, especially games, remains insufficiently studied. Existing benchmarks mainly evaluate correctness through test cases, which are inadequate for GUI applications because these systems are interactive, event-driven, and require correct state transitions across sequences of user actions. Their evaluation therefore should consider interaction flows and UI logic rather than only pass/fail outcomes. To study this problem, we introduce PlayEval, a repository-aware benchmark built from 43 multilingual GUI applications in Python, TypeScript, and JavaScript. Unlike prior GUI benchmarks that are difficult to adapt to desktop environments, PlayEval covers six major GUI application categories and directly supports code-generation evaluation. We further propose Play@k, a metric that measures whether at least one of *k* generated candidates can be played end-to-end without logical errors. To support reliable evaluation, we develop PlayTester, an LLM-based agent that performs task-oriented GUI playthroughs and detects logic violations automatically. Experiments on 10 state-of-the-art code LLMs show that, despite high compilation rates, they achieve near-zero Play@3, revealing major weaknesses in generating logically correct GUI applications. To address this limitation, we present PlayCoder, a multi-agent, repository-aware framework that generates, evaluates, and iteratively repairs GUI application code in a closed loop. PlayCoder substantially improves both functional correctness and semantic alignment for open-source and closed-source models, reaching up to 38.1% Exec@3 and 20.3% Play@3. Case studies further show that it can uncover silent logic bugs missed by traditional metrics and fix them through targeted edits.

cs.SE↗
arXiv2026

Remember Your Trace: Memory-Guided Long-Horizon Agentic Framework for Consistent and Hierarchical Repository-Level Code Documentation

Automated code documentation is essential for modern software development, providing the contextual grounding that both human developers and coding agents rely on to navigate large codebases. Existing repository-level approaches process components independently, causing redundant retrieval and conflicting descriptions across documents while producing outputs that lack hierarchical structure. Therefore, we propose MemDocAgent, a long-horizon agentic framework that generates documentation within a single, integrated context spanning the entire repository. It combines two components: (i) Dependency-Aware Traversal Guiding that predetermines a traversal order respecting dependency and granularity hierarchies; (ii) Memory-Guided Agentic Interaction, in which the agent interacts with RepoMemory, a shared memory accumulating prior work traces through read, write, and verify operations. Through an in-depth multi-criteria evaluation, MemDocAgent achieves the best performance over both open- and closed-source baselines and demonstrates practical applicability in real software development workflows.

cs.SE↗