The Limits of Long-Context Language Models in Code Processing: LONGCODEU as a New Benchmark

The rapid development of Long-Context Language Models (LCLMs) opens up promising possibilities for software development. The ability to process long text passages seems ideal for analyzing and understanding complex code structures. But how powerful are these models actually in handling extensive code? A new benchmark called LONGCODEU aims to shed light on this.

Until now, a comprehensive evaluation framework for measuring the capabilities of LCLMs in the area of code understanding has been lacking. LONGCODEU addresses this gap and offers eight tasks in four categories that cover various aspects of code processing:

The four categories of LONGCODEU:

The perception of code units (e.g., functions, classes): This examines how well the model can identify individual code building blocks.

Understanding within code units: This category investigates whether the model can grasp the logic and functionality within a code unit.

Understanding the relationships between code units: This involves analyzing dependencies and interactions between different code building blocks.

Understanding code documentation that extends over long sections: This category tests the model's ability to interpret the documentation of complex code structures.

As part of the study, nine common LCLMs, including six general and three specifically code-developed models, were evaluated with LONGCODEU. The results reveal significant weaknesses in the processing of long code. In particular, with code lengths of over 32,000 characters, the performance of the models drops drastically, even though some of them are supposed to be able to process context windows of up to 1 million characters.

The biggest challenge for the LCLMs proved to be understanding the relationships between different code units. This suggests that while the models can analyze individual code segments, they have difficulty grasping the complex relationships in larger software projects.

The results of LONGCODEU provide valuable insights for the further development of LCLMs. By identifying the weaknesses, targeted optimizations can be made to improve the performance of the models in the area of code processing. This is an important step in fully exploiting the potential of LCLMs for software development and enabling innovative applications in this area. LONGCODEU offers a solid foundation for future research and contributes to a better understanding of the limits of LCLMs in the context of software development.

Bibliographie: Li, J., Guo, X., Li, L., Zhang, K., Li, G., Li, J., Tao, Z., Liu, F., Tao, C., Zhu, Y., & Jin, Z. (2025). LONGCODEU: Benchmarking Long-Context Language Models on Long Code Understanding. arXiv preprint arXiv:2503.04359. https://arxiv.org/abs/2503.04359 https://arxiv.org/html/2503.04359v1 https://paperswithcode.com/paper/leave-no-document-behind-benchmarking-long https://paperswithcode.com/paper/mmlongbench-doc-benchmarking-long-context https://github.com/MozerWang/Loong https://aclanthology.org/2024.acl-long.859/ https://openreview.net/forum?id=3A71qNKWAS https://github.com/Xuchen-Li/llm-arxiv-daily https://aclanthology.org/2024.emnlp-main.322.pdf https://huggingface.co/papers/2406.11612