ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings
The remarkable advances in AI and Large Language Models (LLMs) have enabled machines to write code, accelerating the growth of software systems. However, the bottleneck in software development is not writing code but understanding it; program understanding is the dominant activity, consuming approximately 70% of developers' time. This implies that improving existing code to make it easier to understand has a high payoff and - in the age of AI-assisted coding - is an essential activity to ensure that a limited pool of developers can keep up with ever-growing codebases.
Authors
- Emma Söderberg (ORCID: https://orcid.org/0000-0001-7966-4560)
- Markus Borg (ORCID: https://orcid.org/0000-0001-7879-4371)
- Adam Tornhill (ORCID: https://orcid.org/0000-0003-2784-9808)
- Nadim Hagatulah
Institutions
- Lund University (SE)
Publication Details
- Published
- 2025-06-23
- DOI
- https://doi.org/10.1145/3696630.3730565
- Primary Topic
- Software Engineering Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- VINNOVA