10 citations · 15 across the 7 of their papers we have counts for
8 papers
Who Finishes the Job? A Study of Follow-Up Fixes and Commit Authorship on AI Coding Agent Pull Requests
Wannita Takerngsaksiri, Nhat Duong, Scott Barnett
AI coding agents now author a large share of pull requests (PRs) merged into popular open-source projects. A merged agent PR is usually considered finished work; yet, prior studies…
TaskEval: Synthesised Evaluation for Foundation-Model Tasks
Dilani Widanapathiranage, Scott Barnett, Stefanus Kurniawan +1
Hallucinations are a key concern when creating applications that rely on Foundation models (FMs). Understanding where and how these subtle failures occur in an application relies o…
Human-In-The-Loop Software Development Agents: Challenges and Future Directions
Jirat Pasuksmit, Wannita Takerngsaksiri, Patanamon Thongtanunam +8
Multi-agent LLM-driven systems for software development are rapidly gaining traction, offering new opportunities to enhance productivity. At Atlassian, we deployed Human-in-the-Loo…
Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian
Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Micheal Fu +3
Software engineers spend a significant amount of time reading code during the software development process, especially in the age of large language models (LLMs) that can automatic…
Human-In-the-Loop Software Development Agents
Wannita Takerngsaksiri, Jirat Pasuksmit, Patanamon Thongtanunam +7
Recently, Large Language Models (LLMs)-based multi-agent paradigms for software engineering are introduced to automatically resolve software development tasks (e.g., from a given i…
PyTester: Deep Reinforcement Learning for Text-to-Testcase Generation
Wannita Takerngsaksiri, Rujikorn Charakorn, Chakkrit Tantithamthavorn +1
Test-driven development (TDD) is a widely-employed software development practice that mandates writing test cases based on requirements before writing the actual code. While writin…