What is the Difference Between Me and You? Benchmarking the Quality Gap Between Human-Written and AI-Generated Code
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arXiv:2608. 06640v1 Announce Type: cross Abstract: The widespread integration of AI coding assistants offers undeniable boosts to engineering velocity.
arXiv:2607. 25130v1 Announce Type: cross Abstract: Imperfections in AI-generated code require that software developers modify the generated code manually, or by re-prompting an AI programming assistant.
The paper investigates how large language models (LLMs) handle bug fixing versus problem solving in competitive programming. Using a dataset of ~3,000 Codeforces submissions and their human fixes, the authors compare LLM-generated patches to human patches and assess whether LLMs prefer to modify buggy code or generate new solutions. Results show that LLMs often alter more lines than necessary and sometimes produce entirely new solutions, performing better when allowed to generate solutions from scratch rather than patching existing code.
arXiv:2606. 12620v1 Announce Type: cross Abstract: Thanks to the rapid adoption of AI code assistants powered by large language models (LLMs), industry codebases are, increasingly, a hybrid of AI- and human-authored code.
The paper investigates how Large Language Models (LLMs) handle bug fixing compared to human-written patches by analyzing about 3,000 Codeforces submissions. It finds that LLMs often modify more lines than necessary and sometimes produce entirely new solutions, and that they solve more problems correctly when generating solutions from scratch rather than patching existing code. The study highlights implications for AI‑assisted programming tools, suggesting a shift toward incremental problem‑solving strategies.
The paper introduces a dataset comprising the complete development history of a 21,000-line Python tool created solely by Claude AI, without any human-authored code or tests. It also presents two code‑provenance tracing tools, three taxonomies for instruction intent, commit provenance, and response reliability, and applies these to analyze the dataset. Findings include that CLI instructions differ from IDE‑chat instructions, development is largely proactive, 14.3% of AI code‑generation events contain errors later caught by the AI‑authored test suite, and about 1 in 4–5 interactive responses contain factual errors.