Position: Future Research and Challenges Remain Towards AI for Software Engineering

Alex Gu, Naman Jain, Wen-Ding Li, Manish Shetty, Kevin Ellis, Koushik Sen, Armando Solar-Lezama

Abstract: AI for software engineering has made remarkable progress, becoming a notable success within generative AI. Despite this, achieving fully automated software engineering is still a significant challenge, requiring research efforts across both academia and industry. In this position paper, our goal is threefold. First, we provide a taxonomy of measures and tasks to categorize work towards AI software engineering. Second, we outline the key bottlenecks permeating today's approaches. Finally, we highlight promising paths towards making progress on these bottlenecks to guide future research in this rapidly maturing field.

Lay Summary: AI for software engineering has made remarkable progress, becoming a notable success within generative AI. Despite this, achieving fully automated software engineering is still a significant challenge, requiring research efforts across both academia and industry. In this position paper, our goal is threefold. First, we provide a taxonomy of measures and tasks to categorize work towards AI software engineering. Second, we outline the key bottlenecks permeating today's approaches. Finally, we highlight promising paths towards making progress on these bottlenecks to guide future research in this rapidly maturing field.

Verify Author Names: My co-authors have confirmed that their names are spelled correctly both on OpenReview and in the camera-ready PDF. (If needed, please update ‘Preferred Name’ in OpenReview to match the PDF.)

No Additional Revisions: I understand that after the May 29 deadline, the camera-ready submission cannot be revised before the conference. I have verified with all authors that they approve of this version.

Pdf Appendices: My camera-ready PDF file contains both the main text (not exceeding the page limits) and all appendices that I wish to include. I understand that any other supplementary material (e.g., separate files previously uploaded to OpenReview) will not be visible in the PMLR proceedings.

Latest Style File: I have compiled the camera ready paper with the latest ICML2025 style files <https://media.icml.cc/Conferences/ICML2025/Styles/icml2025.zip> and the compiled PDF includes an unnumbered Impact Statement section.

Paper Verification Code: NjU0N

Primary Area: Research Priorities, Methodology, and Evaluation

Keywords: ai for code

Submission Number: 401