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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…
AI coding agents such as Codex and Claude Code are increasingly used to autonomously contribute to software repositories. However, little is known about how repository-level configuration artifacts affect operational efficiency of the…
Artificial Intelligence (AI) is making a significant impact in multiple areas like medical, military, industrial, domestic, law, arts as AI is capable to perform several roles such as managing smart factories, driving autonomous vehicles,…
Continuous Integration (CI) is a software development practice that builds and tests software frequently (e.g., at every push). One main motivator to adopt CI is the potential to deliver software functionalities more quickly than not using…
Developing test code may be a time-consuming task that usually requires much effort and cost, especially when it is done manually. Besides, during this process, developers and testers are likely to adopt bad design choices, which may lead…
GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this…
In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with Software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and…
AI coding agents allow software developers to generate code quickly, which raises a practical question for project managers and open source maintainers: can vibe coders with less development experience substitute for expert developers? To…
Proof engineering is notoriously labor-intensive: proofs that are straightforward on paper often require lengthy scripts in theorem provers. Recent advances in large language models (LLMs) create new opportunities for proof automation:…
AI agents have recently shown significant promise in software engineering. Much public attention has been transfixed on the topic of code generation from Large Language Models (LLMs) via a prompt. However, software engineering is much more…
Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are transforming the software engineering landscape. These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks.…
Representational State Transfer (REST) APIs are a cornerstone of modern cloud native systems. Ensuring their reliability demands automated test suites that exercise diverse and boundary level behaviors. Nevertheless, designing such test…
LLM-based coding agents are increasingly used to generate code, tests, and documentation. Still, their outputs can be plausible yet misaligned with developer intent and provide limited evidence for review in evolving projects. This limits…
Background. Pull requests are a common practice for contributing and reviewing contributions, and are employed both in open-source and industrial contexts. One of the main goals of code reviews is to find defects in the code, allowing…
Autonomous multi-agent AI systems are poised to transform various industries, particularly software development and knowledge work. Understanding current perceptions among professionals is crucial for anticipating adoption challenges,…
Cloud high quality API (Application Programming Interface) testing is essential for supporting the API economy. Autotest is a random test generator that addresses this need. It reads the API specification and deduces a model used in the…
While prior work has examined the generation capabilities of Agentic AI systems, little is known about how reviewers respond to AI-authored code in practice. In this paper, we present a large-scale empirical study of code review dynamics in…
The rapid adoption of AI coding agents is fundamentally shifting software developers' roles from code authors to code reviewers. While developers spend a significant portion of their time reading and comprehending code, the linguistic…
Automated testing is crucial for maintaining open-source software quality. However, motivating contributors to include tests for code changes remains a challenge. While existing interventions, such as code coverage metrics and reviewer…
Fine-tuning large language models for code editing has typically relied on mining commits and pull requests. The working hypothesis has been that commit messages describe human intent in natural language, and patches to code describe the…