Bibliographic References Repository - Triaxial Evaluation of Large Language Models in Code Generation: A Structural and Semantic Approach.

Abstract

This repository provides the bibliographic dataset and methodological documentation supporting the research article *"Triaxial Evaluation of Large Language Models in Code Generation: A Structural and Semantic Approach"*. The dataset consists of a comprehensive BibTeX (`.bib`) file containing a hybrid corpus of scientific literature focused on the evaluation of AI-generated code, specifically regarding structural complexity, semantic correctness, and the application of Large Language Models (LLMs). The bibliographic collection was constructed through a dual-methodology approach: 1. **Automated Search (IEEE Xplore):** Executed between April 6 and April 20, 2026, targeting metadata fields (Abstract and Document Title) for key benchmarks such as "HumanEval", "SWE-bench", "LiveCodeBench", as well as queries intersecting LLMs, evaluation metrics, and coding.2. **Manual Curation:** Integration of state-of-the-art preprints (ArXiv) concerning LLM-as-a-judge evaluation paradigms, alongside seminal software engineering literature detailing classical complexity metrics (e.g., McCabe's Cyclomatic Complexity, Halstead's Software Science). Essential technical documentation and official repositories for the evaluated tools are also included. The provided `.bib` file represents the raw, unfiltered extraction prior to the application of the research's strict exclusion criteria, which subsequently isolated papers exclusively addressing metrics for AI-generated code. A `README.md` file is included in this repository, detailing the exact search strings, temporal scope, and the exclusion methodology required to reproduce the literature review process.