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ACE : automated technical debt remediation with validated large language model refactorings

Tornhill, Adam ; Borg, Markus LU ; Hagatulah, Nadim LU orcid and Söderberg, Emma LU orcid (2025) p.1318-1324
Abstract
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.

This paper introduces Augmented Code Engineering (ACE), a tool that automates code improvements using validated LLM output. Developed through a data-driven approach,... (More)
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.

This paper introduces Augmented Code Engineering (ACE), a tool that automates code improvements using validated LLM output. Developed through a data-driven approach, ACE provides reliable refactoring suggestions by considering both objective code quality improvements and program correctness. Early feedback from users suggests that AI-enabled refactoring helps mitigate code-level technical debt that otherwise rarely gets acted upon. (Less)
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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
FSE Companion '25 : Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering - Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering
pages
1318 - 1324
publisher
Association for Computing Machinery (ACM)
ISBN
979-8-4007-1276-0
DOI
10.1145/3696630.3730565
language
English
LU publication?
yes
id
5b05e57f-1d71-4e04-9620-b81a8b7680ea
date added to LUP
2025-08-18 13:42:17
date last changed
2025-09-25 15:34:27
@inproceedings{5b05e57f-1d71-4e04-9620-b81a8b7680ea,
  abstract     = {{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.<br/><br/>This paper introduces Augmented Code Engineering (ACE), a tool that automates code improvements using validated LLM output. Developed through a data-driven approach, ACE provides reliable refactoring suggestions by considering both objective code quality improvements and program correctness. Early feedback from users suggests that AI-enabled refactoring helps mitigate code-level technical debt that otherwise rarely gets acted upon.}},
  author       = {{Tornhill, Adam and Borg, Markus and Hagatulah, Nadim and Söderberg, Emma}},
  booktitle    = {{FSE Companion '25 : Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering}},
  isbn         = {{979-8-4007-1276-0}},
  language     = {{eng}},
  month        = {{07}},
  pages        = {{1318--1324}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{ACE : automated technical debt remediation with validated large language model refactorings}},
  url          = {{http://dx.doi.org/10.1145/3696630.3730565}},
  doi          = {{10.1145/3696630.3730565}},
  year         = {{2025}},
}