Search arXiv⌕ Search

arXiv · 2610.14462

Rethinking SZZ Search Strategies: Lessons from Claude Code

Abstract

Identifying bug-inducing commits (BICs) is important for studying software bugs and maintaining software, yet the accuracy of existing SZZ methods remains limited. Although various improvements have been proposed, many SZZ methods still rely on code tracing, which searches for BICs by tracing code changed in a bug-fixing commit (BFC) back to earlier commits. However, this approach may miss the actual BIC when the code responsible for introducing a bug differs from the code modified to fix it. To overcome this limitation, we investigate whether BIC identification can be improved by exploring search strategies that are not constrained to start with code tracing. We use Claude Code, a coding agent that has demonstrated high BIC identification accuracy in prior research, to explore such strategies. Rather than prescribing a search process or providing predefined candidate commits, we allow Claude Code to independently investigate a Git repository. We evaluate its identification accuracy and search behavior on 79 Java cases from 70 repositories. In 79.7% of runs, Claude Code reviewed commit histories before inspecting individual code diffs. It used commit messages to select changes for closer inspection. With this approach, Claude Code achieves an F1 score of 0.771, significantly outperforming all ten evaluated SZZ baselines. Our failure analysis also reveals incorrect oracle labels and ambiguities in BIC definitions in the studied evaluation dataset. After reconstructing the evaluation dataset to address these issues, Claude Code achieves an F1 score of 0.938. A key lesson from Claude Code for improving SZZ search strategies is to use semantic information to find relevant commits and then examine their code changes, rather than starting with code tracing.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Inase Kondo, Masanari Kondo, Daniel M. German, Olivier Nourry, Yasutaka Kamei, Yoshiki Higo. 2026-10-10. Rethinking SZZ Search Strategies: Lessons from Claude Code. https://arxiv.org/abs/2610.14462

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

KEEP EXPLORING

Related papers

Enhancing Knowledge Density via Hierarchical Context Pruning for Retrieval-Augmented Repository-Level Code Completion

Retrieval-Augmented Generation (RAG) has advanced repository-level code completion by retrieving external code snippets, API documentation, or type definitions to expand the model's contextual awareness. However, under the finite token budget of the context window, existing retrieval-augmented methods often saturate the budget with highly similar or even redundant code, which not only wastes tokens but also suppresses critical cross-file dependencies, preventing synergistic interaction between semantically similar reference snippets and cross-file dependency definitions. Therefore, further gains are limited by low knowledge density in the retrieved context rather than by model capacity. We propose SaraCoder, a retrieval-augmented framework that enhances knowledge density via hierarchical context pruning under a fixed context budget. A Hierarchical Feature Optimization module prunes an over-complete candidate pool in four stages. The freed tokens are then allocated to an External-Aware Identifier Disambiguator, which selectively injects only the most necessary cross-file dependency definitions. SaraCoder transforms context construction into a budget-aware optimization problem, balancing reference snippets and cross-file dependencies to build a context that is both knowledge-dense and semantically complete. Experiments on CrossCodeEval and RepoEval-Updated, across multiple backbone code LLMs, show that SaraCoder consistently outperforms strong RAG baselines such as GraphCoder and Repocoder in both code-level and identifier-level metrics, without increasing token usage, and yields synergistic gains when combined with other cross-file methods.

cs.SE↗

Multi-LLM Orchestration for High-Quality Code Generation: Exploiting Complementary Model Strengths

Large Language Models (LLMs) are central to automated code generation, yet most approaches use one model at every stage, from writing code to repairing and optimizing it. LLMs have complementary strengths: their failures do not fully overlap across programming languages, algorithmic categories, and development stages. For example, in Python, the model with the highest code-generation accuracy ranks last at code repair. Using one model to generate code, another to repair it, and a third to refine it can therefore achieve higher correctness and better execution performance than a single model throughout. We propose PerfOrch, a multi-LLM multi-agent system with four agents for categorization, generation, debugging, and refinement. The generation, debugging, and refinement agents select models through a Memory module that ranks candidate LLMs by their measured performance for each stage, programming language, and algorithmic category. We evaluate PerfOrch with five candidate LLMs on 4,640 problems from four benchmarks covering five programming languages (HumanEval-X, EffiBench-X, BigCodeBench, and APPS) against single-model direct generation, single-model generate-debug-refine pipelines (PerfCodeGen), and the state-of-the-art multi-agent systems MapCoder and CodeSIM. PerfOrch achieves the highest pass@1 in all twelve benchmark-language settings, exceeding the strongest baseline by an average of 8.19 percentage points, and reduces execution time for an average of 75.03% of solved problems with an average speedup of 1.18x. The Memory rankings remain effective on three unseen benchmarks without re-profiling. Sequential refinement matches exhaustive execution-time refinement coverage with 66% fewer refinement-stage tokens (57% for average-memory optimization). The complementary strengths persist after all five LLMs are replaced with their current-generation successors.

cs.SE↗

Smart Hiring Redefined: An Intelligent Recruitment Management Platform

Against the backdrop of deepening digital and intelligent transformation in human resource management, traditional recruitment models struggle to fully meet enterprises' growing demand for precise talent acquisition due to limited efficiency, high costs, and information asymmetry. As a vital tool for optimizing recruitment processes, reducing labor and time costs, and enhancing core competitiveness, intelligent recruitment management systems have become an indispensable component of modern organizational talent strategies. Compared with the labor intensive tasks of resume screening, candidate position matching, and interview coordination in traditional manual recruitment, intelligent recruitment systems significantly enhance the efficiency and accuracy of the hiring process through automation and data driven approaches. These systems enable rapid parsing of massive resume volumes, intelligent matching of candidates to positions, and automated scheduling of interview processes. This substantially reduces the workload on human resources departments while improving recruitment quality and response speed. This research leverages the Java technology framework to design and implement an intelligent recruitment management system tailored for campus recruitment scenarios. The system establishes a collaborative platform connecting students, enterprises, and administrators through information technology and intelligent solutions, offering comprehensive functionalities including job posting distribution, resume submission, candidate position matching, and process management. Guided by the vision of Smart Campus Recruitment, the project delivers a more convenient job seeking experience for students and provides enterprises with more efficient talent screening and recruitment management services, thereby driving high quality development in university enterprise collaboration.

cs.SE↗