Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering

Technical Summary: Towards Comprehensive Benchmarking Infrastructure for LLMs in Software Engineering

Problem Statement
The rapid advancement of Large Language Models for code (LLMc) has outpaced the development of reliable evaluation methodologies. Current benchmarking practices suffer from three primary deficiencies that obscure critical gaps in model robustness, interpretability, fairness, efficiency, and real-world usability:

1. Lack of Specialized SE Datasets: Existing datasets predominantly focus on isolated source code and natural language pairs, neglecting essential software engineering (SE) attributes such as repository structures, build configurations, commit histories, and project-specific constraints. This results in benchmarks that assess syntactic correctness but fail to evaluate integration into real software projects.
2. Overreliance on ML-Centric Metrics: Evaluation is heavily skewed toward accuracy, precision, recall, and text similarity (e.g., BLEU, CodeBLEU). These metrics fail to capture semantic equivalence, partial correctness, or critical SE dimensions like maintainability, security, and bias. Furthermore, the "SWE-bench Illusion" suggests that high accuracy often stems from memorization of curated solutions rather than genuine reasoning.
3. Absence of Standardized Data Pipelines: Benchmark construction is fragmented, with each research group building infrastructure from scratch. This leads to inconsistent preprocessing, undocumented data provenance, widespread data contamination (leakage), and excessive resource overhead (often 50–75% of research effort), hindering reproducibility and fair comparison.

Methodology
To address these challenges, the authors employed a dual-method approach combining a comprehensive survey of existing benchmarks with insights from a dedicated community workshop.

• Survey: The authors analyzed benchmarks across five core SE tasks: code generation, bug fixing, vulnerability detection, test generation, and agent/multi-task systems. They reviewed prominent benchmarks (e.g., HumanEval, SWE-bench, LiveCodeBench, SecBench) published in leading conferences (ICSE, ASE, FSE, TOSEM).
• Workshop Synthesis: The authors synthesized perspectives from community experts to identify infrastructure gaps and formulate requirements for a next-generation evaluation ecosystem.
• Framework Proposal: Based on these findings, the authors proposed BEHELM (Benchmarking for Holistic Evaluation of Large Language Models for Code), a unified infrastructure designed to standardize scenario specification and multi-metric evaluation.

Key Contributions

1. Synthesis of Infrastructure Gaps: The paper identifies and articulates three critical barriers to reliable LLMc evaluation: the absence of SE-rich datasets, the dominance of narrow ML metrics, and the lack of standardized data engineering pipelines.
2. Comprehensive Benchmark Survey: A detailed analysis of current benchmarks reveals their limitations, such as data contamination in "live" benchmarks (e.g., LiveCodeBench showing 15–20% performance drops on post-cutoff data) and the inability of single-reference solutions to capture intent-based evaluation.
3. The BEHELM Framework: The authors introduce a holistic infrastructure comprising two interconnected components:
   • Software Scenario Specification: A systematic taxonomy decomposing evaluation contexts into five properties: Software Task (e.g., bug fixing, translation), Programming Language, Input Granularity (e.g., tokens, snippets), Output Granularity (e.g., methods, files), and I/O Type (e.g., code-to-code, text-to-code).
   • Multi-Metric Evaluation: A shift from single-metric assessment to a multidimensional space evaluating six dimensions: Accuracy, Efficiency, Interpretability, Bias, Fairness, and Robustness.
4. Community Recommendations: Concrete guidelines for developing sustainable benchmarking infrastructure, including automated pipelines for data extraction and deduplication, rigorous provenance tracking, and standardized reporting formats.

Results and Findings
The study highlights specific empirical observations from the surveyed landscape:

• Contamination Effects: Models show significant performance degradation (15–20%) on tasks released after their training cutoffs, indicating that current high scores often reflect memorization.
• Task Difficulty Discrepancies: While top models achieve >70% on curated subsets like SWE-bench Verified, performance drops to ~23% on SWE-bench Pro (using private, recent code), revealing a gap between benchmark performance and real-world capability.
• Metric Limitations: Test-passing rates are insufficient proxies for code quality; for instance, SecBench found that even the best LLMs achieved only 49% success in generating proof-of-concept exploits and 34% in patching vulnerabilities.
• Inefficiency: The manual burden of dataset creation is unsustainable, with studies showing up to 75% of project time consumed by data engineering rather than novel evaluation methods.

Significance and Claims
The paper claims that the proposed BEHELM infrastructure is significant because it moves the field away from fragmented, narrow evaluations toward a unified, reproducible, and realistic assessment framework.

• Holistic Assessment: By decoupling scenario definition from metric selection, BEHELM allows researchers to assess models across diverse software engineering contexts and quality dimensions simultaneously.
• Reduced Overhead: The framework aims to lower the barrier to entry for creating new benchmarks by providing standardized tools for profiling, comparing, and debugging model behavior, thereby reducing the redundant effort currently spent on dataset construction.
• Future-Proofing: The infrastructure is designed to support the evaluation of modern AI-assisted development tools (e.g., agentic systems) through process-aware, milestone-based assessments rather than binary success/failure metrics.

The authors position BEHELM not as a single static benchmark, but as a community-driven hub intended to unify existing efforts and facilitate the creation of new, high-quality evaluations that reflect the complexity of real-world software engineering.