arXiv:2511.10868v2 Announce Type: replace Abstract: Training data imbalance poses a major challenge for code LLMs. Most available data heavily over represents raw opensource code while underrepresenting broader software engineering tasks, especially in low resource languages like Golang. As a result, models excel at code autocompletion but struggle with real world developer workflows such as unit test generation. To address this gap, we introduce GO UT Bench, a benchmark dataset of 5264 pairs of code and unit tests, drawn from 10 permissively licensed Golang repositories spanning diverse domai
The proliferation of LLMs and increasing demand for their application in software engineering tasks, coupled with the recognized limitations of current models in specific domains like unit test generation, makes this a timely development.
This development addresses a critical bottleneck in the practical application of LLMs for software development, potentially accelerating developer workflows and improving code quality for specific programming languages.
The availability of a specialized, high-quality dataset for Go unit test generation means LLMs trained on it will become significantly more capable in this area, directly impacting software development efficiency.
- · Go developers
- · Go-centric software companies
- · AI model developers
- · Software engineering tooling
- · Traditional manual unit test generation methods
LLMs will improve significantly in generating Go unit tests, reducing developer effort.
Increased adoption of LLM-powered tools in Go development, potentially leading to similar datasets for other 'low resource' languages.
A shift in software engineering education and practice towards leveraging AI for foundational tasks, freeing developers for higher-level design and architecture.
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Read at arXiv cs.LG