AI for Code
Most enterprises rely on large volumes of aging code that are a challenge to debug, maintain, and update. At IBM Research, we’re using AI to help modernize software stacks, designing automated programming and refactoring systems to help businesses keep up with the speed of modern life.
Our work
These new IBM agents will give developers new ways to solve problems — and clear their backlog
NewsMike MurphyWhat’s an LLM context window and why is it getting larger?
NewsKim MartineauSoftware has eaten the world. What now?
Q & AKim MartineauCOBOL programmers are getting harder to find. IBM’s code-writing AI can help
NewsKim MartineauFind and fix IT glitches before they crash the system
NewsKim MartineauContinuing the momentum of AI for Code with Project Wisdom
NewsRuchir Puri- See more of our work on AI for Code
Publications
Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights
- Rahul Krishna
- Rangeet Pan
- et al.
- 2025
- FSE 2025
LlamaRestTest: Effective REST API Testing with Small Language Models
- Myeongsoo Kim
- Saurabh Sinha
- et al.
- 2025
- FSE 2025
Can LLMs Replace Manual Annotation of Software Engineering Artifacts?
- Toufique Ahmed
- Premkumar Devanbu
- et al.
- 2025
- MSR 2025
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL
- Tyler Stennett
- Myeongsoo Kim
- et al.
- 2025
- ICSE 2025
ASTER: Natural and Multi-language Unit Test Generation with LLMs
- Rangeet Pan
- Myeongsoo Kim
- et al.
- 2025
- ICSE 2025
A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs
- Myeongsoo Kim
- Tyler Stennett
- et al.
- 2025
- ICSE 2025