EVIDENCE-GUIDED LOCAL CODE GENERATION AND REFINEMENT: INTEGRATING DETERMINISTIC GUARDRAILS, BOUNDED GRAPH RETRIEVAL, AND DIAGNOSTIC-AWARE EVALUATION

This study investigates a resource-conscious approach to local code generation and refinementthat integrates three complementary mechanisms: Bounded, task-directed graph retrieval forselecting repository context; deterministic guardrails and execution-based validation for checkingproposed changes; and diagnostic-aware evaluation to distinguish final task outcomes fromobservable properties of the development process.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-31
DOI
https://doi.org/10.5281/zenodo.23833482
Primary Topic
Software Engineering Research
Type
preprint
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preprint

EVIDENCE-GUIDED LOCAL CODE GENERATION AND REFINEMENT: INTEGRATING DETERMINISTIC GUARDRAILS, BOUNDED GRAPH RETRIEVAL, AND DIAGNOSTIC-AWARE EVALUATION

Sai Ishaan Balaji
Zenodo (CERN European Organization for Nuclear Research)
Software Engineering Research
preprint

EVIDENCE-GUIDED LOCAL CODE GENERATION AND REFINEMENT: INTEGRATING DETERMINISTIC GUARDRAILS, BOUNDED GRAPH RETRIEVAL, AND DIAGNOSTIC-AWARE EVALUATION

Sai Ishaan Balaji
preprint en

Abstract

This study investigates a resource-conscious approach to local code generation and refinementthat integrates three complementary mechanisms: Bounded, task-directed graph retrieval forselecting repository context; deterministic guardrails and execution-based validation for checkingproposed changes; and diagnostic-aware evaluation to distinguish final task outcomes fromobservable properties of the development process.

Zenodo (CERN European Organization for Nuclear Research)
Software Engineering Research
DOIOpen Access PDFOpenAlex
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EVIDENCE-GUIDED LOCAL CODE GENERATION AND REFINEMENT: INTEGRATING DETERMINISTIC GUARDRAILS, BOUNDED GRAPH RETRIEVAL, AND DIAGNOSTIC-AWARE EVALUATION — Sai Ishaan Balaji · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS