Pith. sign in

REVIEW 5 cited by

Knowledge-Enhanced Program Repair for Data Science Code

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.09771 v1 pith:4SOO26UD submitted 2025-02-13 cs.SE

Shuyin Ouyang , Jie M. Zhang , Zeyu Sun , Albert Merono Penuela This is my paper
classification cs.SE
keywords knowledgedsrepairrepaircodedatallmssciencegraph
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper introduces DSrepair, a knowledge-enhanced program repair method designed to repair the buggy code generated by LLMs in the data science domain. DSrepair uses knowledge graph based RAG for API knowledge retrieval as well as bug knowledge enrichment to construct repair prompts for LLMs. Specifically, to enable knowledge graph based API retrieval, we construct DS-KG (Data Science Knowledge Graph) for widely used data science libraries. For bug knowledge enrichment, we employ an abstract syntax tree (AST) to localize errors at the AST node level. DSrepair's effectiveness is evaluated against five state-of-the-art LLM-based repair baselines using four advanced LLMs on the DS-1000 dataset. The results show that DSrepair surpasses all five baselines. Specifically, when compared to the second-best baseline, DSrepair demonstrates significant improvements, fixing 44.4%, 14.2%, 20.6%, and 32.1% more buggy code snippets for each of the four evaluated LLMs, respectively. Additionally, it achieves greater efficiency, reducing the number of tokens required per code task by 17.49%, 34.24%, 24.71%, and 17.59%, respectively.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Discussion to Execution: Replicating Buggy and Correct Data Science Code

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Reprodgen reconstructs executable buggy-patched code pairs from Q&A posts, reaching about 58% LLM-judged buggy-code success and 52% patched-code success on a new 176-program benchmark.

  2. SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair

    cs.SE 2026-02 conditional novelty 6.0 of 10

    A three-agent locate-suggest-fix framework with a knowledge-graph toolkit resolves 154/300 SWE-Bench-Lite issues with Claude-3.5, outperforming same-model baselines by 5-10 points.

  3. ViTAD: Timing Violation-Aware Debugging of RTL Code using Large Language Models

    cs.AR 2025-08 unverdicted novelty 5.0 of 10

    ViTAD uses a graph of signal timing dependencies plus an LLM to diagnose and repair RTL timing violations, reportedly improving success rate by 19.30 points over a basic LLM baseline.

  4. Beyond Execution: Static-Analysis Rewards and Hint-Conditioned Diffusion RL for Code Generation

    cs.SE 2026-05 unverdicted novelty 4.0 of 10

    Static checking rewards and moderate AST-based hints improve diffusion RL performance for code generation, with effectiveness varying by task difficulty across HumanEval, MBPP, and LiveCodeBench.

  5. Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering

    cs.SE 2025-08 reject novelty 3.0 of 10

    A survey-plus-benchmark argues that current code LLMs fail safety thresholds and need stronger governance, but the supporting experiment is incomplete.

Pith tools