[Paper] Assessing and Improving the Representativeness of Code Generation Benchmarks Using Knowledge Units (KUs) of Programming Languages -- An Empirical Study

Source: arXiv - 2601.03780v1

Overview

Large language models (LLMs) such as GPT‑4 and Claude are now being judged on how well they can write code, usually with benchmarks like HumanEval or MBPP. This paper asks a simple but crucial question: Do those benchmarks actually reflect the breadth of programming concepts developers use every day? By mapping benchmark tasks to Knowledge Units (KUs)—the fundamental language constructs and API patterns that make up real code—the authors reveal a striking mismatch and propose a way to fix it.

Key Contributions

• KU‑based taxonomy: Defined 20 Knowledge Units that capture core Python language features and standard‑library APIs.
• Empirical coverage analysis: Measured KU coverage in HumanEval and MBPP versus 30 open‑source Python projects, showing benchmarks cover only ~50 % of the KUs while real projects use all of them.
• Distributional imbalance detection: Demonstrated that benchmark tasks are heavily skewed toward a few KUs, unlike the balanced distribution seen in production code.
• Prompt‑driven task synthesis: Built a LLM‑powered framework that generates new coding tasks targeting under‑represented KUs, creating 440 additional tasks.
• Benchmark augmentation & re‑evaluation: Augmented HumanEval/MBPP with the synthesized tasks, achieving a >60 % improvement in KU distribution alignment and exposing a 12–45 % performance drop for state‑of‑the‑art LLMs.
• Actionable guidelines: Provided concrete recommendations for constructing more realistic code‑generation benchmarks.

Methodology

1. Define Knowledge Units (KUs) – The authors grouped related Python language constructs (e.g., loops, comprehensions, exception handling) and common library APIs (e.g., os, json, datetime) into 20 cohesive units.
2. Extract KU usage – Using static analysis, they identified which KUs appear in each benchmark task and in each of the 30 real‑world projects (selected for diversity in domain and size).
3. Coverage & distribution analysis – They computed the proportion of KUs present and the frequency distribution across tasks, comparing benchmarks to the projects.
4. Task synthesis framework – Leveraging a powerful LLM (GPT‑4), they crafted prompts that explicitly request code‑generation problems exercising a target KU while keeping difficulty comparable to existing benchmark items.
5. Augmentation & evaluation – The newly generated 440 tasks were added to HumanEval and MBPP. The authors then re‑ran several leading code‑generation models (e.g., GPT‑4, Claude, LLaMA‑2) on the original and augmented suites, measuring pass rates and statistical significance of performance changes.

Results & Findings

In plain terms, the original benchmarks were “over‑specialized,” causing models to look better than they would on a more representative set of programming challenges. When the benchmarks were balanced, even the strongest LLMs struggled noticeably more.

Practical Implications

• More reliable hiring tests – Companies using HumanEval‑style assessments to gauge candidate or model ability should be aware that scores may be inflated if the test does not cover the full spectrum of language features they’ll encounter on the job.
• Better model fine‑tuning – Developers can enrich training data with KU‑balanced examples, potentially reducing blind spots in LLMs (e.g., handling exceptions or using less‑common standard‑library modules).
• Benchmark design – Future code‑generation benchmarks should adopt a KU‑centric checklist to ensure coverage and balanced difficulty, leading to fairer leaderboards and research comparisons.
• Tooling for automated task generation – The prompt‑based framework can be repurposed to create custom benchmark suites tailored to a project’s domain (e.g., data‑science‑heavy libraries vs. web‑framework code).
• Risk mitigation – By exposing the over‑optimistic performance of current models, the study encourages more cautious deployment of LLMs in safety‑critical code generation (e.g., security‑related scripts).

Limitations & Future Work

• Language scope – The study focuses exclusively on Python; extending the KU taxonomy to other languages (JavaScript, Java, Rust) may reveal different gaps.
• Static analysis granularity – Some KUs (especially those involving dynamic typing or reflection) are hard to capture statically, possibly under‑estimating coverage.
• Task difficulty calibration – While prompts aimed for comparable difficulty, subtle differences could affect model performance; a more rigorous difficulty metric would strengthen conclusions.
• Human validation – The synthesized tasks were not exhaustively vetted by expert programmers; future work could incorporate human review to ensure realism and relevance.
• Iterative benchmark evolution – The authors suggest a feedback loop where model failures inform the next round of KU‑targeted task generation, an avenue ripe for exploration.

Authors

• Md Ahasanuzzaman
• Bram Adams
• Emad Fallahzadeh
• Gustavo A. Oliva
• Ahmed E. Hassan

Paper Information

• arXiv ID: 2601.03780v1
• Categories: cs.SE
• Published: January 7, 2026
• PDF: Download PDF