# Quality evaluation of Tabby coding assistant using real source code snippets

arXiv: https://arxiv.org/abs/2504.08650 · HTML: https://mlsanity.com/paper/2504.08650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development](https://mlsanity.com/paper/2601.13597/) (2026)
- [Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code](https://mlsanity.com/paper/2607.09902/) (2026)

## Abstract (arXiv)

Large language models have become a popular tool in software development, providing coding assistance. The proper measurement of the accuracy and reliability of the code produced by such tools is a challenge due to natural language prompts. We propose a simple pipeline that uses state-of-the-art implementation of classic and universal genres of algorithms and data structures. We focus on measuring the quality of TabbyML code assistant due to its open licence and the flexibility in the choice of the language model. Our results presented as cyclomatic complexity, Halstead's Bugs & Effort and four text-based similarity matrices depict the usability of TabbyML in coding assistance tasks.