AI builds, We Analyze: An Empirical Study of AI-Generated Build Code Quality
Fuente:
arXiv
Saved in:
| Main Authors: | Ghammam, Anwar, Almukhtar, Mohamed |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Quality and Security Signals in AI-Generated Python Refactoring Pull Requests
by: Almukhtar, Mohamed, et al.
Published: (2026)
by: Almukhtar, Mohamed, et al.
Published: (2026)
From Code Changes to Quality Gains: An Empirical Study in Python ML Systems with PyQu
by: Almukhtar, Mohamed, et al.
Published: (2025)
by: Almukhtar, Mohamed, et al.
Published: (2025)
Build Code Needs Maintenance Too: A Study on Refactoring and Technical Debt in Build Systems
by: Ghammam, Anwar, et al.
Published: (2025)
by: Ghammam, Anwar, et al.
Published: (2025)
An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?
by: Suh, Hyunjae, et al.
Published: (2024)
by: Suh, Hyunjae, et al.
Published: (2024)
An Empirical Study on the Impact of Gender Diversity on Code Quality in AI Systems
by: Cynthia, Shamse Tasnim, et al.
Published: (2025)
by: Cynthia, Shamse Tasnim, et al.
Published: (2025)
Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild
by: Liu, Yue, et al.
Published: (2026)
by: Liu, Yue, et al.
Published: (2026)
Testing with AI Agents: An Empirical Study of Test Generation Frequency, Quality, and Coverage
by: Yoshimoto, Suzuka, et al.
Published: (2026)
by: Yoshimoto, Suzuka, et al.
Published: (2026)
A Large-Scale Empirical Study of AI-Generated Code in Real-World Repositories
by: Mao, Tianhao, et al.
Published: (2026)
by: Mao, Tianhao, et al.
Published: (2026)
Factors Influencing the Quality of AI-Generated Code: A Synthesis of Empirical Evidence
by: Geruslu, Vehid, et al.
Published: (2026)
by: Geruslu, Vehid, et al.
Published: (2026)
Do AI Coding Agents Log Like Humans? An Empirical Study
by: Ouatiti, Youssef Esseddiq, et al.
Published: (2026)
by: Ouatiti, Youssef Esseddiq, et al.
Published: (2026)
Usage, Effects and Requirements for AI Coding Assistants in the Enterprise: An Empirical Study
by: Vukovic, Maja, et al.
Published: (2026)
by: Vukovic, Maja, et al.
Published: (2026)
An Empirical Study of Generative AI Adoption in Software Engineering
by: Giray, Görkem, et al.
Published: (2025)
by: Giray, Görkem, et al.
Published: (2025)
Guiding AI to Fix Its Own Flaws: An Empirical Study on LLM-Driven Secure Code Generation
by: Yan, Hao, et al.
Published: (2025)
by: Yan, Hao, et al.
Published: (2025)
Quality In, Quality Out: Investigating Training Data's Role in AI Code Generation
by: Improta, Cristina, et al.
Published: (2025)
by: Improta, Cristina, et al.
Published: (2025)
Generating Java Methods: An Empirical Assessment of Four AI-Based Code Assistants
by: Corso, Vincenzo, et al.
Published: (2024)
by: Corso, Vincenzo, et al.
Published: (2024)
Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI
by: Zhang, Ruixin, et al.
Published: (2026)
by: Zhang, Ruixin, et al.
Published: (2026)
Automated Prompt Generation for Code Intelligence: An Empirical study and Experience in WeChat
by: Ji, Kexing, et al.
Published: (2025)
by: Ji, Kexing, et al.
Published: (2025)
Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study
by: AlMulla, Buthayna, et al.
Published: (2025)
by: AlMulla, Buthayna, et al.
Published: (2025)
On the Quality of AI-Generated Source Code Comments: A Comprehensive Evaluation
by: Guelman, Ian, et al.
Published: (2024)
by: Guelman, Ian, et al.
Published: (2024)
Analyzing Prominent LLMs: An Empirical Study of Performance and Complexity in Solving LeetCode Problems
by: Guimaraes, Everton, et al.
Published: (2025)
by: Guimaraes, Everton, et al.
Published: (2025)
Lessons from Building StackSpot AI: A Contextualized AI Coding Assistant
by: Pinto, Gustavo, et al.
Published: (2023)
by: Pinto, Gustavo, et al.
Published: (2023)
Agentic Refactoring: An Empirical Study of AI Coding Agents
by: Horikawa, Kosei, et al.
Published: (2025)
by: Horikawa, Kosei, et al.
Published: (2025)
An Empirical Study of False Negatives and Positives of Static Code Analyzers From the Perspective of Historical Issues
by: Cui, Han, et al.
Published: (2024)
by: Cui, Han, et al.
Published: (2024)
Harnessing Hype to Teach Empirical Thinking: An Experience With AI Coding Assistants
by: Wyrich, Marvin, et al.
Published: (2026)
by: Wyrich, Marvin, et al.
Published: (2026)
The Impact of Generative AI on Code Expertise Models: An Exploratory Study
by: Cury, Otávio, et al.
Published: (2025)
by: Cury, Otávio, et al.
Published: (2025)
How Are We Doing With Using AI-Based Programming Assistants For Privacy-Related Code Generation? The Developers' Experience
by: Madampe, Kashumi, et al.
Published: (2025)
by: Madampe, Kashumi, et al.
Published: (2025)
Enhancing Code Annotation Reliability: Generative AI's Role in Comment Quality Assessment Models
by: Killivalavan, Seetharam, et al.
Published: (2024)
by: Killivalavan, Seetharam, et al.
Published: (2024)
Can We Classify Flaky Tests Using Only Test Code? An LLM-Based Empirical Study
by: Berndt, Alexander, et al.
Published: (2026)
by: Berndt, Alexander, et al.
Published: (2026)
An Empirical Study on the Impact of Code Duplication-aware Refactoring Practices on Quality Metrics
by: AlOmar, Eman Abdullah
Published: (2025)
by: AlOmar, Eman Abdullah
Published: (2025)
Can AI Agents Generate Microservices? How Far are We?
by: Adnan, Bassam, et al.
Published: (2026)
by: Adnan, Bassam, et al.
Published: (2026)
Philosophical Dispositions as Behavioral Constraints for AI-Assisted Code Review: An Empirical Study
by: Bansal, Kaushal
Published: (2026)
by: Bansal, Kaushal
Published: (2026)
Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents
by: Cynthia, Shamse Tasnim, et al.
Published: (2026)
by: Cynthia, Shamse Tasnim, et al.
Published: (2026)
Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests
by: Gong, Jingzhi, et al.
Published: (2026)
by: Gong, Jingzhi, et al.
Published: (2026)
Building Living Software Systems with Generative & Agentic AI
by: White, Jules
Published: (2024)
by: White, Jules
Published: (2024)
Are Coding Agents Generating Over-Mocked Tests? An Empirical Study
by: Hora, Andre, et al.
Published: (2026)
by: Hora, Andre, et al.
Published: (2026)
An Empirical Study of the Non-determinism of ChatGPT in Code Generation
by: Ouyang, Shuyin, et al.
Published: (2023)
by: Ouyang, Shuyin, et al.
Published: (2023)
An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
by: Yang, Zezhou, et al.
Published: (2025)
by: Yang, Zezhou, et al.
Published: (2025)
AI-Generated Code Is Not Reproducible (Yet): An Empirical Study of Dependency Gaps in LLM-Based Coding Agents
by: Vangala, Bhanu Prakash, et al.
Published: (2025)
by: Vangala, Bhanu Prakash, et al.
Published: (2025)
Enhancing Code Quality with Generative AI: Boosting Developer Warning Compliance
by: Chang, Hansen, et al.
Published: (2025)
by: Chang, Hansen, et al.
Published: (2025)
Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis
by: Sabra, Abbas, et al.
Published: (2025)
by: Sabra, Abbas, et al.
Published: (2025)
Similar Items
-
Quality and Security Signals in AI-Generated Python Refactoring Pull Requests
by: Almukhtar, Mohamed, et al.
Published: (2026) -
From Code Changes to Quality Gains: An Empirical Study in Python ML Systems with PyQu
by: Almukhtar, Mohamed, et al.
Published: (2025) -
Build Code Needs Maintenance Too: A Study on Refactoring and Technical Debt in Build Systems
by: Ghammam, Anwar, et al.
Published: (2025) -
An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?
by: Suh, Hyunjae, et al.
Published: (2024) -
An Empirical Study on the Impact of Gender Diversity on Code Quality in AI Systems
by: Cynthia, Shamse Tasnim, et al.
Published: (2025)