Hajimu Iida

Kyogo Horikawa, Kosei Horikawa, Yutaro Kashiwa et al.

Code readability is fundamental to software quality and maintainability. Poor readability extends development time, increases bug-inducing risks, and contributes to technical debt. With the rapid advancement of Large Language Models, AI agent-based approaches have emerged as a promising paradigm for automated refactoring, capable of decomposing complex tasks through autonomous planning and execution. While prior studies have examined refactoring by AI agents, these analyses cover all forms of refactoring, including performance optimization and structural improvement. As a result, the extent to which AI agent-based refactoring specifically improves code readability remains unclear. This study investigates the impact of AI agent-based refactoring on code readability. We extracted commits containing readability-related keywords from the AIDev dataset and analyzed changes in readability metrics before and after each commit, covering 403 commits evaluated using multiple quantitative metrics. Our results indicate that AI agents primarily target logic complexity (42.4%) and documentation improvements (24.2%) rather than surface-level aspects like naming conventions or formatting. However, contrary to expectations, readability-focused commits often degraded traditional quality metrics: the Maintainability Index decreased in 56.1% of commits, while Cyclomatic Complexity increased in 42.7%.

Suzuka Yoshimoto, Shun Fujita, Kosei Horikawa et al.

Agent-based coding tools have transformed software development practices. Unlike prompt-based approaches that require developers to manually integrate generated code, these agent-based tools autonomously interact with repositories to create, modify, and execute code, including test generation. While many developers have adopted agent-based coding tools, little is known about how these tools generate tests in real-world development scenarios or how AI-generated tests compare to human-written ones. This study presents an empirical analysis of test generation by agent-based coding tools using the AIDev dataset. We extracted 2,232 commits containing test-related changes and investigated three aspects: the frequency of test additions, the structural characteristics of the generated tests, and their impact on code coverage. Our findings reveal that (i) AI authored 16.4% of all commits adding tests in real-world repositories, (ii) AI-generated test methods exhibit distinct structural patterns, featuring longer code and a higher density of assertions while maintaining lower cyclomatic complexity through linear logic, and (iii) AI-generated tests contribute to code coverage comparable to human-written tests, frequently achieving positive coverage gains across several projects.

Shota Sawada, Tatsuya Shirai, Yutaro Kashiwa et al.

LLM-based autonomous coding agents have reshaped software development. While these agents excel at code generation, open questions persist about the long-term maintainability of AI-generated code. This study empirically investigates the maintenance extent, human involvement, and modification types of AI-generated files versus human-authored code. Using the AIDev dataset of AI-generated pull requests and GitHub, we analyzed over 1,000 files and approximately 3,200 changes from 100 popular repositories. Our findings show that: (i) AI-generated files receive less frequent maintenance than human-authored code, with updates affecting only a small fraction of file size; (ii) the most frequent modifications to AI code are feature extensions, whereas human updates focus on bug fixes, and (iii) human developers perform the large majority of this maintenance.

Yasushi Tanaka, Hajimu Iida, Yasuhiro Takemura

We conducted a Project-Based Learning (PBL)-type exercise incorporating Japanese cartoon (manga) techniques into Requirements Development (RD) processes. Manga has established techniques, such as those for character setting and story development, that we thought are also valid for RD processes. Using this manga-driven method, students were able to clarify high-level project goals early in the development life-cycle, and succeeded in defining high quality and unique system ideas.