Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Programming Languages, Computer Science - Software Engineering

In recent years, large language models (LLMs) have showcased significant advancements in code generation. However, most evaluation benchmarks are primarily oriented towards Python, making it difficult to evaluate other programming languages, such as Swift, with high quality. By examining widely established multilingual benchmarks like HumanEval-XL and MultiPL-E, we identified critical issues specific to their Swift components, making them insufficient or even irrelevant for assessing LLM coding capabilities on Swift. Unlike these existing approaches, which prioritize rapid scaling and generalization by automatically translating Python-centric benchmarks with LLMs, we adopt a quality-over-quantity methodology. We present SwiftEval, the first Swift-oriented benchmark consisting of 28 carefully hand-crafted problems, and evaluate 44 popular Code LLMs on it. Our results show significant LLM scores drop for problems requiring language-specific features, most noticeable in the models of smaller sizes.
Comment: Accepted to FORGE'25 Benchmarking on 15.01.2025, to be published by IEEE under the CC BY-NC-ND 4.0 license. This is the accepted version of the article (5 pages, 2 figures, 1 table). DOI will be added upon publication