Thursday 05 June 2025
Scientists have made a significant breakthrough in developing a new benchmark for testing code generation models. This innovative tool, called YABLoCo (Yet Another Benchmark for Long Context Code Generation), aims to measure the performance of large language models (LLMs) in generating code for real-world software projects.
Traditionally, benchmarks have focused on small-scale coding tasks, such as auto-completion or code snippet generation. However, modern software development often involves working with massive repositories containing thousands of lines of code. YABLoCo fills this gap by providing a comprehensive benchmark that simulates the complexity and context of real-world codebases.
The benchmark consists of four large-scale code repositories in C/C++ languages, featuring a total of 215 functions across various projects. Each function is accompanied by metadata, including the name, description, language, and dependencies. This contextual information allows LLMs to better understand the coding task at hand and generate more accurate code.
One of the key features of YABLoCo is its ability to simulate different levels of context dependency for functions. This means that LLMs can be tested on a range of scenarios, from simple standalone functions to complex ones that rely on multiple dependencies across the repository.
The benchmark’s evaluation pipeline is designed to efficiently calculate metrics such as pass@k, which measures the percentage of generated code that matches the original code. This allows researchers and developers to assess the performance of LLMs in a more comprehensive and meaningful way.
In experiments with various LLMs, YABLoCo showed that incorporating repository context can significantly improve code generation quality. For example, one model’s pass@k score increased by 10-20% when using the oracle context – a simulated representation of the original function’s dependencies.
The development of YABLoCo has significant implications for the field of software engineering. It enables researchers to create more realistic and challenging evaluation scenarios for LLMs, ultimately leading to better-performing code generation models. Additionally, the benchmark can be used by developers to fine-tune their own code completion tools and improve the overall quality of their software projects.
In summary, YABLoCo represents a major step forward in developing benchmarks for large language models in code generation. Its unique features and evaluation pipeline make it an essential tool for researchers and developers seeking to push the boundaries of code intelligence.
Cite this article: “YABLoCo: A Comprehensive Benchmark for Large Language Models in Code Generation”, The Science Archive, 2025.
Code Generation, Large Language Models, Benchmarking, Software Engineering, Code Completion, Contextual Information, Repository Simulation, Dependency Analysis, Pass@K Score, Oracle Context.
