Code comprehension is vital for software development. Still, unreadable code remains a significant issue, costing substantial time and money losses. While tools to identify code exhibiting a low readability exist, take actions to improve such a quality aspect is far from trivial. To support developers in such a task, Vitale et al. introduced at ASE’23 an approach using a transformer model (T5) fine-tuned on code commits in which developers explicitly stated their goal to improve code readability. The authors reported that their model is able to generate readability-improving changes being identical to those implemented by developers (exact matches) in 21% of cases and that, even when they differ, they still improve readability in the vast majority of cases (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\sim \)</EquationSource> </InlineEquation>80%). Given the major advances in AI made in the last few years, we questioned whether a fine-tuning for such a task was still needed in the era of Large Language Models (LLMs), thus revisiting the work by Vitale et al. with state-of-the-art models. In doing so, we found out one major issue with the original study design: The training-test splitting was performed randomly (i.e., readability-improving commits mined from open source projects were randomly split between training and test) rather than by project, significantly inflating the reported performance due to repeating readability-improving commits done within the same project. Indeed, as we will show, even newer and more powerful LLMs fine-tuned for this task can only achieve a <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\sim \)</EquationSource> </InlineEquation>1% of exact matches. Once fixed this issue, we shifted our attention on assessing whether fine-tuning on available developer readability improvements is actually needed given the recent advances in general-purpose LLMs. We compare the performance of fine-tuned state-of-the-art LLMs with what they achieve in zero-shot setting, also including in our study commercial LLMs (GPT-4.1). As we will show, quantitative metrics (e.g., exact matches, CrystalBLEU) tell very little about the readability-improving capabilities of LLMs and, thus, our study is mainly qualitative in nature, with a total of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\sim \)</EquationSource> </InlineEquation>9,500 inspected LLMs’ change recommendations. While all models do improve code readability, our results clearly show that fine-tuning using mined readability-improving commits does not help and, instead, results in sensibly poorer readability recommendations as compared to LLMs used in a zero-shot setting, with GPT-4.1 being the one achieving the best results.

Revisiting code readability improvement with LLMs: A critical assessment of fine-tuned models

期刊论文
出版模式：

开放获取
发表日期：

2026年7月23日

查看全文

Antonio Vitale,
Emanuela Guglielmi,
Valentina Piantadosi,
Antonio Mastropaolo,
Gabriele Bavota,
Rocco Oliveto,
Simone Scalabrino

摘要

Code comprehension is vital for software development. Still, unreadable code remains a significant issue, costing substantial time and money losses. While tools to identify code exhibiting a low readability exist, take actions to improve such a quality aspect is far from trivial. To support developers in such a task, Vitale et al. introduced at ASE’23 an approach using a transformer model (T5) fine-tuned on code commits in which developers explicitly stated their goal to improve code readability. The authors reported that their model is able to generate readability-improving changes being identical to those implemented by developers (exact matches) in 21% of cases and that, even when they differ, they still improve readability in the vast majority of cases ( \(\sim \) 80%). Given the major advances in AI made in the last few years, we questioned whether a fine-tuning for such a task was still needed in the era of Large Language Models (LLMs), thus revisiting the work by Vitale et al. with state-of-the-art models. In doing so, we found out one major issue with the original study design: The training-test splitting was performed randomly (i.e., readability-improving commits mined from open source projects were randomly split between training and test) rather than by project, significantly inflating the reported performance due to repeating readability-improving commits done within the same project. Indeed, as we will show, even newer and more powerful LLMs fine-tuned for this task can only achieve a \(\sim \) 1% of exact matches. Once fixed this issue, we shifted our attention on assessing whether fine-tuning on available developer readability improvements is actually needed given the recent advances in general-purpose LLMs. We compare the performance of fine-tuned state-of-the-art LLMs with what they achieve in zero-shot setting, also including in our study commercial LLMs (GPT-4.1). As we will show, quantitative metrics (e.g., exact matches, CrystalBLEU) tell very little about the readability-improving capabilities of LLMs and, thus, our study is mainly qualitative in nature, with a total of \(\sim \) 9,500 inspected LLMs’ change recommendations. While all models do improve code readability, our results clearly show that fine-tuning using mined readability-improving commits does not help and, instead, results in sensibly poorer readability recommendations as compared to LLMs used in a zero-shot setting, with GPT-4.1 being the one achieving the best results.