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arXiv:2608.18645 (cs)
[Submitted on 19 Aug 2026]

Title:Code Health in LLM-Based Test Generation: Effectiveness and Token Efficiency

Authors:Freya Wirdemann, Markus Borg, Nadim Hagatulah, Adam Tornhill
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Abstract:Coding agents powered by Large Language Models (LLMs) are now prominent in software engineering. Previous work has shown that AI tools perform better on high-quality source code that is easy to maintain. In this study, we investigate how the effectiveness of LLM-generated unit tests varies across maintainability levels measured by CodeScene's CodeHealth (CH). We assess test effectiveness using traditional coverage metrics and mutation score across Python, Java, and C++. Moreover, we study how code with different levels of CH translates into input tokens using common industrial tokenizers. Our results suggest that CH provides a weak but consistent signal of LLM-generated test effectiveness and is negatively correlated with input-token count. These findings provide further evidence for a relationship between maintainability and LLM-based software development.
Comments: Accepted at the Engineering Track of the 26th IEEE International Conference on Source Code Analysis and Manipulation (SCAM 2026)
Subjects: Software Engineering (cs.SE)
Cite as: arXiv:2608.18645 [cs.SE]
  (or arXiv:2608.18645v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2608.18645

Submission history

From: Markus Borg [view email]
[v1] Wed, 19 Aug 2026 07:47:43 UTC (352 KB)
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