ACE: automated technical debt remediation with validated large language model refactorings
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| Název: | ACE: automated technical debt remediation with validated large language model refactorings |
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| Autoři: | Tornhill, Adam, Borg, Markus, Hagatulah, Nadim, Söderberg, Emma |
| Přispěvatelé: | Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Computer Science, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för datavetenskap, Originator, Lund University, Faculty of Engineering, LTH, Competence centers, LTH, NEXTG2COM – a Vinnova Competence Centre in Advanced Digitalisation, Lunds universitet, Lunds Tekniska Högskola, Kompetenscentrum, LTH, NEXTG2COM – ett Vinnova kompetenscenter inom Avancerad Digitalisering, Originator, Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Computer Science, Software Engineering Research Group, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för datavetenskap, Programvarusystem, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Originator, Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Computer Science, Software Development and Environments, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för datavetenskap, Programvaruteknik, Originator |
| Zdroj: | FSE Companion '25. :1318-1324 |
| Témata: | Natural Sciences, Computer and Information Sciences, Software Engineering, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Programvaruteknik |
| Popis: | 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. |
| Databáze: | SwePub |
Nájsť tento článok vo Web of Science | Abstrakt: | 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. |
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| DOI: | 10.1145/3696630.3730565 |