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Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go

arXiv 2511.10868 ↗First posted 14 Nov 2025cs.LGcited by 0

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Abstract (arXiv)

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 domain. We evaluate its effectiveness as a fine tuning dataset across two LLM families i.e. mixture of experts and dense decoders. Our results show that finetuned models outperform their base counterparts on more than 75% of benchmark tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 14 Nov 2025.

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References · 16

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  6. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  7. 2023LIMA: Less Is More for Alignmentcited by 1,100
  8. 2022CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesiscited by 885
  9. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 721
  10. 2023Instruction Tuning for Large Language Models: A Surveycited by 623
  11. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 406
  12. 2023MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Datacited by 286
4 more
  1. 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agentscited by 133
  2. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 101
  3. 2024CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarkscited by 23
  4. 2024API-guided Dataset Synthesis to Finetune Large Code Modelscited by 11

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  1. 2025Learning to Generate Unit Tests for Automated Debugging
  2. 2026CangjieBench: Benchmarking LLMs on a Low-Resource General-Purpose Programming Language
  3. 2025Unify and Triumph: Polyglot, Diverse, and Self-Consistent Generation of Unit Tests with LLMs
  4. 2025EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code
  5. 2025Beyond Synthetic Benchmarks: Evaluating LLM Performance on Real-World Class-Level Code Generation
  6. 2026Synthesizing File-Level Data for Unit Test Generation with Chain-of-Thoughts via Self-Debugging
  7. 2023LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation
  8. 2026TAM-Eval: Evaluating LLMs for Automated Unit Test Maintenance