FSOFT AI Center

🔥 ACM SIGSOFT DISTINGUISHED PAPER AWARD RECOGNIZES FPT AI RESIDENCY RESEARCH AT ICSE 2026

💫 We are proud to share that our AI Residency community has achieved a landmark success: our research paper has been accepted to the International Conference on Software Engineering (ICSE 2026) and honored with the ACM SIGSOFT Distinguished Paper Award. This work was selected among the Top 1.5% of submissions (22 out of 1469), underscoring its technical excellence, clarity, and transformative potential.

👉 Our Awarded Research Paper
Title: SWE-Synth: Synthetic Repo-level Bug Dataset for Training Automated Program Repair Models

Authors: Minh Pham* (AI Residency Alumni), Huy Phan* (AI Residency Alumni), Hoang Phan, Cuong Le (AI Residency Alumni), Tien N. Nguyen (mentor), Nghi Bui (mentor)
*co-first-author

Abstract: Large language models (LLMs) are revolutionizing automated program repair (APR) by localizing bugs, generating patches, and verifying fixes. Yet progress has been constrained by the absence of scalable, verifiable datasets. SWE-Synth introduces a groundbreaking framework for synthesizing realistic, repository-level bug-fix datasets enriched with test cases and structured repair trajectories. By leveraging LLM agents to simulate debugging workflows, SWE-Synth generates high-quality, process-aware data at scale - a breakthrough that strengthens the real-world viability of autonomous bug-fixing systems and accelerates innovation in software engineering automation.

🌐 ICSE - rank A* is widely recognized as the premier global forum for software engineering*, bringing together top-tier researchers, practitioners, and educators to share cutting-edge advancements, trends, and real-world experiences. In 2026, ICSE will take place from April 12 - 18 in Rio de Janeiro, Brazil, with the core conference running April 15 -17.

✨ This achievement not only underscores the strength of our AI‑first research but also demonstrates how synthetic, agent‑generated datasets can fundamentally reshape the future of autonomous software repair, driving greater reliability, productivity, and innovation across the industry. The influence of SWE‑Synth reaches far beyond its technical contributions: by offering a scalable benchmark, it accelerates progress in AI‑driven software reliability and strengthens the development of autonomous bug‑fixing models. At the same time, the dataset empowers the creation of AI‑powered developer tools capable of intelligently debugging, patching, and verifying code, reducing manual effort while boosting efficiency. For FPT Software and its clients, integrating SWE‑Synth into AI‑first workflows paves the way for next‑generation engineering solutions that enhance reliability, shorten development cycles, and lower costs. Beyond enterprise impact, its open‑source availability catalyzes broader innovation, enabling the global research community to push forward the frontier of AI‑powered software development.