YABLoCo: Yet Another Benchmark for Long Context Code Generation
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arXiv
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| Format: | Preprint |
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2025
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| author | Valeev, Aidar Garaev, Roman Lomshakov, Vadim Piontkovskaya, Irina Ivanov, Vladimir Adewuyi, Israel |
| author_facet | Valeev, Aidar Garaev, Roman Lomshakov, Vadim Piontkovskaya, Irina Ivanov, Vladimir Adewuyi, Israel |
| contents | Large Language Models demonstrate the ability to solve various programming tasks, including code generation. Typically, the performance of LLMs is measured on benchmarks with small or medium-sized context windows of thousands of lines of code. At the same time, in real-world software projects, repositories can span up to millions of LoC. This paper closes this gap by contributing to the long context code generation benchmark (YABLoCo). The benchmark featured a test set of 215 functions selected from four large repositories with thousands of functions. The dataset contained metadata of functions, contexts of the functions with different levels of dependencies, docstrings, functions bodies, and call graphs for each repository. This paper presents three key aspects of the contribution. First, the benchmark aims at function body generation in large repositories in C and C++, two languages not covered by previous benchmarks. Second, the benchmark contains large repositories from 200K to 2,000K LoC. Third, we contribute a scalable evaluation pipeline for efficient computing of the target metrics and a tool for visual analysis of generated code. Overall, these three aspects allow for evaluating code generation in large repositories in C and C++. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04406 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | YABLoCo: Yet Another Benchmark for Long Context Code Generation Valeev, Aidar Garaev, Roman Lomshakov, Vadim Piontkovskaya, Irina Ivanov, Vladimir Adewuyi, Israel Computation and Language Artificial Intelligence Software Engineering Large Language Models demonstrate the ability to solve various programming tasks, including code generation. Typically, the performance of LLMs is measured on benchmarks with small or medium-sized context windows of thousands of lines of code. At the same time, in real-world software projects, repositories can span up to millions of LoC. This paper closes this gap by contributing to the long context code generation benchmark (YABLoCo). The benchmark featured a test set of 215 functions selected from four large repositories with thousands of functions. The dataset contained metadata of functions, contexts of the functions with different levels of dependencies, docstrings, functions bodies, and call graphs for each repository. This paper presents three key aspects of the contribution. First, the benchmark aims at function body generation in large repositories in C and C++, two languages not covered by previous benchmarks. Second, the benchmark contains large repositories from 200K to 2,000K LoC. Third, we contribute a scalable evaluation pipeline for efficient computing of the target metrics and a tool for visual analysis of generated code. Overall, these three aspects allow for evaluating code generation in large repositories in C and C++. |
| title | YABLoCo: Yet Another Benchmark for Long Context Code Generation |
| topic | Computation and Language Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2505.04406 |