Automated Codebase Reconciliation using Large Language Models
Bibliographic record
Abstract
Large-scale software projects frequently encounter the challenge of manually propagating code changes across branches—a process that is error-prone due to code divergence, conflicting dependencies, and branch-specific modifications. Automating code porting can streamline development workflows, accelerate development cycles, and improve team collaboration. However, achieving this automation presents significant hurdles, particularly in maintaining consistency and resolving conflicts during codebase integration. We propose a novel approach that integrates algorithmic analysis with artificial intelligence-driven code generation, leveraging multi-agent systems to automate the identification of porting requirements and the development of ‘context-aware’ modifications. Our comprehensive, end-to-end framework starts by extracting recent commits to evaluate divergence. It subsequently assesses the necessity for porting changes and employs large language model (LLM) based systems to generate adaptive code suggestions tailored to files exhibiting inconsistencies. Experimental results suggest a substantial decrease in manual work through pipeline-generated pull requests. Despite these promising outcomes, integrating LLMs into complex workflows presents challenges, such as handling intricate dependencies and ensuring alignment with a company’s software development issue tracking and change management systems. This paper explores the potential and limitations of LLMs in advancing automation within software engineering and suggests future directions for enhancing these models to achieve industry-grade reliability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".