arXiv subjects

Tatsuya Shirai

Publications and source records attributed to Tatsuya Shirai.

4 recordsLinked to original sources

arXiv2026

To What Extent Does Agent-generated Code Require Maintenance? An Empirical Study

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.

cs.SE↗

arXiv2026

What to Cut? Predicting Unnecessary Methods in Agentic Code Generation

Agentic Coding, powered by autonomous agents such as GitHub Copilot and Cursor, enables developers to generate code, tests, and pull requests from natural language instructions alone. While this accelerates implementation, it produces larger volumes of code per pull request, shifting the burden from implementers to reviewers. In practice, a notable portion of AI-generated code is eventually deleted during review, yet reviewers must still examine such code before deciding to remove it. No prior work has explored methods to help reviewers efficiently identify code that will be removed.In this paper, we propose a prediction model that identifies functions likely to be deleted during PR review. Our results show that functions deleted for different reasons exhibit distinct characteristics, and our model achieves an AUC of 87.1%. These findings suggest that predictive approaches can help reviewers prioritize their efforts on essential code.

cs.SE↗

arXiv2026

Does Programming Language Matter? An Empirical Study of Fuzzing Bug Detection

Fuzzing has become a popular technique for automatically detecting vulnerabilities and bugs by generating unexpected inputs. In recent years, the fuzzing process has been integrated into continuous integration workflows (i.e., continuous fuzzing), enabling short and frequent testing cycles. Despite its widespread adoption, prior research has not examined whether the effectiveness of continuous fuzzing varies across programming languages. This study conducts a large-scale cross-language analysis to examine how fuzzing bug characteristics and detection efficiency differ among languages. We analyze 61,444 fuzzing bugs and 999,248 builds from 559 OSS-Fuzz projects categorized by primary language. Our findings reveal that (i) C++ and Rust exhibit higher fuzzing bug detection frequencies, (ii) Rust and Python show low vulnerability ratios but tend to expose more critical vulnerabilities, (iii) crash types vary across languages and unreproducible bugs are more frequent in Go but rare in Rust, and (iv) Python attains higher patch coverage but suffers from longer time-to-detection. These results demonstrate that fuzzing behavior and effectiveness are strongly shaped by language design, providing insights for language-aware fuzzing strategies and tool development.

cs.SE↗

arXiv2025

Large-Scale Empirical Analysis of Continuous Fuzzing: Insights from 1 Million Fuzzing Sessions

Software vulnerabilities are constantly being reported and exploited in software products, causing significant impacts on society. In recent years, the main approach to vulnerability detection, fuzzing, has been integrated into the continuous integration process to run in short and frequent cycles. This continuous fuzzing allows for fast identification and remediation of vulnerabilities during the development process. Despite adoption by thousands of projects, however, it is unclear how continuous fuzzing contributes to vulnerability detection. This study aims to elucidate the role of continuous fuzzing in vulnerability detection. Specifically, we investigate the coverage and the total number of fuzzing sessions when fuzzing bugs are discovered. We collect issue reports, coverage reports, and fuzzing logs from OSS-Fuzz, an online service provided by Google that performs fuzzing during continuous integration. Through an empirical study of a total of approximately 1.12 million fuzzing sessions from 878 projects participating in OSS-Fuzz, we reveal that (i) a substantial number of fuzzing bugs exist prior to the integration of continuous fuzzing, leading to a high detection rate in the early stages; (ii) code coverage continues to increase as continuous fuzzing progresses; and (iii) changes in coverage contribute to the detection of fuzzing bugs. This study provides empirical insights into how continuous fuzzing contributes to fuzzing bug detection, offering practical implications for future strategies and tool development in continuous fuzzing.

cs.SE↗