Enhancing Software Maintainability through LLM-Assisted Code Refactoring
High code quality, particularly in terms of maintainability, is crucial for ensuring that software remains efficient and adaptable over time, while minimizing long-term maintenance costs. As artificial intelligence continues to evolve, its application in software development offers new opportunities to improve code quality. This study investigates the use of Large Language Models (LLMs) to enhance software maintainability through code refactoring. The results indicate that LLMs can be effectively utilized for this purpose, with effectiveness varying depending on the model and the evaluation metric used. Although the study is based on a limited set of Python projects and specific prompting strategies, it provides a meaningful step toward understanding the broader applicability of LLMs in this context.
Tue 2 DecDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
09:30 - 11:00 | Technical Debt and RefactoringShort Papers and Posters / Research Papers at Sala degli Affreschi (Fresco Room) Chair(s): Sousuke Amasaki Nanzan University | ||
09:30 15mTalk | Enhancing Python Code Maintainability through Large Language Model-Based Approaches Research Papers | ||
09:45 15mTalk | Enhancing Software Maintainability through LLM-Assisted Code Refactoring Research Papers Tommaso Fulcini Politecnico di Torino, Riccardo Coppola Politecnico di Torino, Flavio Giobergia Politecnico di Torino, Amirali Changizi Politecnico di Torino, Meelad Dashti Politecnico di Torino, Kimia Dorrani Politecnico di Torino, Domenico Amalfitano University of Naples Federico II, Damiano Distante UnitelmaSapienza University of Rome, Filippo Ricca DIBRIS, Università di Genova | ||
10:00 15mTalk | Temporal Evolution of Architectural Complexity and Technical Debt in Microservices: An Exploratory Case Study Research Papers Bhuwan Paudel Blekinge Institute of Technology, Javier Gonzalez-Huerta Blekinge Institute of Technology, Ehsan Zabardast Nordea / Blekinge Institute of Technology | ||
10:15 15mTalk | Detecting Technical Debt in Source Code Changes using Large Language Models Research Papers Merve Astekin SINTEF, Arda Goknil SINTEF Digital, Sagar Sen , Simeon Tverdal SINTEF Digital, Phu Nguyen SINTEF | ||
10:30 7mTalk | LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code Short Papers and Posters Shahbaz Siddeeq Tampere University, Muhammad Waseem Faculty of Information Technology and Communication Sciences, Tampere University, 33014 Tampere, Finland, Zeeshan Rasheed Tampere University, Md Mahade Hasan Tampere University, Jussi Rasku Tampere University, Mika Saari Tampere University, Henri Terho Eficode Oy, Kalle Mäkelä Eficode Oy, Kai-Kristian Kemell Tampere University, Pekka Abrahamsson Tampere University | ||
10:37 7mTalk | Architecture Degradation at Scale: Challenges and Insights from Practice Short Papers and Posters Ehsan Zabardast Nordea / Blekinge Institute of Technology, Bhuwan Paudel Blekinge Institute of Technology, Javier Gonzalez-Huerta Blekinge Institute of Technology DOI Authorizer link File Attached | ||
10:44 7mTalk | How Well Small Language Models Can Be Adapted for Software Maintenance and Refactoring Tasks Short Papers and Posters Gabija Asvydyte University of Groningen, Sushant Kumar Pandey University of Groningen, The Netherlands, Sivajeet Chand Technical University of Munich | ||