4 citations · 9 across the 8 of their papers we have counts for
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What Types of Code Review Comments Do Developers Most Frequently Resolve?
Saul Goldman, Hong Yi Lin, Jirat Pasuksmit +11
Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes.…
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…
A Systematic Literature Review on Reasons and Approaches for Accurate Effort Estimations in Agile
Jirat Pasuksmit, Patanamon Thongtanunam, Shanika Karunasekera
Background: Accurate effort estimation is crucial for planning in Agile iterative development. Agile estimation generally relies on consensus-based methods like planning poker, whi…
AI for DevSecOps: A Landscape and Future Opportunities
Michael Fu, Jirat Pasuksmit, Chakkrit Tantithamthavorn
DevOps has emerged as one of the most rapidly evolving software development paradigms. With the growing concerns surrounding security in software systems, the DevSecOps paradigm ha…