Pith Number

pith:FAY6BY37

pith:2026:FAY6BY375PZCNLFFMNABFX2N7T

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Fine-Tuning Models for Automated Code Review Feedback

Hind Zantout, Manuel Maarek, Michael A Lones, Smitha S Kumar

Parameter-efficient fine-tuning of Code Llama produces feedback on buggy Java code that students rate as effective as ChatGPT.

arxiv:2605.12610 v1 · 2026-05-12 · cs.SE

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3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

Our findings indicate that PEFT leads to notable improvements in feedback quality and significantly outperforms prompt engineering, providing an avenue for developing freely deployable feedback tools that can be effectively used to guide student learning. Student evaluation indicates that learners value the PEFT model's feedback and see it as being equally effective as the proprietary ChatGPT model.

C2weakest assumption

That the combination of BLEU, ROUGE, BERTScore, manual annotation, and student ratings reliably measures the actual educational value and long-term learning impact of the generated feedback.

C3one line summary

PEFT fine-tuning of Code Llama yields feedback on student Java bugs that students judge equal to ChatGPT and better than prompt engineering, using BLEU/ROUGE/BERTScore plus human ratings.

References

40 extracted · 40 resolved · 5 Pith anchors

[1] Marzieh Ahmadzadeh, Dave Elliman, and Colin Higgins. 2005. An analysis of patterns of debugging among novice computer science students. InProceedings of the 10th Annual SIGCSE Conference on Innovation 2005 · doi:10.1145/1067445.1067472

[2] Zishan Ahmed, Shakib Sadat Shanto, and Akinul Islam Jony. 2024. Potentiality of generative AI tools in higher education: Evaluating ChatGPT’s viability as a teaching assistant for introductory program 2024 · doi:10.3934/steme.2024011

[3] Amjad Altadmri and Neil C.C. Brown. 2015. 37 Million Compilations: Investigat- ing Novice Programming Mistakes in Large-Scale Student Data. InProceedings of the 46th ACM Technical Symposium on Compute 2015 · doi:10.1145/2676723.2677258

[4] Using thematic analysis in psychology 2006 · doi:10.1191/1478088706qp063oa

[5] Brown and Amjad Altadmri 2014 · doi:10.1145/2632320.2632343

Receipt and verification

First computed	2026-05-18T03:10:00.647248Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2831e0e37febf226aca5634012df4dfcca278a21387cee6c92be0d897f651962

Aliases

arxiv: 2605.12610 · arxiv_version: 2605.12610v1 · doi: 10.48550/arxiv.2605.12610 · pith_short_12: FAY6BY375PZC · pith_short_16: FAY6BY375PZCNLFF · pith_short_8: FAY6BY37

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FAY6BY375PZCNLFFMNABFX2N7T \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2831e0e37febf226aca5634012df4dfcca278a21387cee6c92be0d897f651962

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "1ef5dac0430c06c4c989778abfc63bbb4197c70c279112e51deba8e27a14823c",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.SE",
    "submitted_at": "2026-05-12T18:02:04Z",
    "title_canon_sha256": "cb845b260f8bb528fdadff076abbe8abe923780da70104cccdd6a41e99f2c668"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12610",
    "kind": "arxiv",
    "version": 1
  }
}