Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered ReportRegistered Reports Paper
[Background/Context] AI assistants like GitHub Copilot are transforming software engineering; several studies have highlighted productivity improvements. However, their impact on code quality, particularly in terms of maintainability, requires further investigation. [Objective/Aim] This study aims to examine the influence of AI assistants on software maintainability, specifically assessing how these tools affect the ability of developers to evolve code. [Method] We will conduct a two-phased controlled experiment involving professional developers. In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant. Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants. We will employ Bayesian analysis to evaluate differences in completion time, perceived productivity, code quality, and test coverage.
Fri 11 OctDisplayed time zone: Mountain Time (US & Canada) change
10:30 - 12:00 | Session 12: Machine Learning in Software EngineeringTool Demo Track / Research Track / New Ideas and Emerging Results Track / Registered Reports Track at Abineau Chair(s): Mohammed Sayagh ETS Montreal, University of Quebec | ||
10:30 15m | Can We Do Better with What We Have Done? Unveiling the Potential of ML Pipeline in NotebooksResearch Track Paper Research Track | ||
10:45 10m | MergeRepair: Merging Task-Specific Adapters in Code LLMs for Automated Program RepairRegistered Reports Paper Registered Reports Track Meghdad Dehghan University of British Columbia, Jie JW Wu University of British Columbia (UBC), Fatemeh Hendijani Fard University of British Columbia, Ali Ouni ETS Montreal, University of Quebec Pre-print | ||
10:55 15m | On the Use of Deep Learning Models for Semantic Clone DetectionResearch Track Paper Research Track Subroto Nag Pinku University of Saskatchewan, Debajyoti Mondal , Chanchal K. Roy University of Saskatchewan, Canada | ||
11:10 10m | GlueTest: Testing Code Translation via Language InteroperabilityNIER Paper New Ideas and Emerging Results Track Muhammad Salman Abid Cornell University, Mrigank Pawagi Indian Institute of Science, Bengaluru, Sugam Adhikari Islington College, Xuyan Cheng Dickinson College, Ryed Badr University of Illinois Urbana Champaign, Md Wahiduzzaman BRAC University, Vedant Rathi Adlai E Stevenson High School, Ronghui Qi Wuhan University, Choiyin Li Po Leung Kuk Ngan Po Ling College, Lu Liu University of Washington, Rohit Sai Naidu Dublin High School, Licheng Lin Zhejiang University, Que Liu University of Shanghai for Science and Technology, Asif Zubayer Palak BRAC University, Mehzabin Haque University of Dhaka, Xinyu Chen University of Illinois Urbana Champaign, Darko Marinov University of Illinois at Urbana-Champaign, Saikat Dutta Cornell University | ||
11:20 10m | Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered ReportRegistered Reports Paper Registered Reports Track Markus Borg CodeScene, Dave Hewett Equal Experts, Donald Graham Equal Experts, Noric Couderc Lund University, Emma Söderberg Lund University, Luke Church University of Cambridge | Candela Inc, Dave Farley Continuous Delivery Pre-print | ||
11:30 15m | Leveraging Large Vision-Language Model For Better Automatic Web GUI TestingResearch Track Paper Research Track Siyi Wang , Sinan Wang Southern University of Science and Technology, Yujia Fan , Xiaolei Li , Yepang Liu Southern University of Science and Technology | ||
11:45 5m | StackRAG Agent: Improving Developer Answers with Retrieval-Augmented GenerationTool Demo Paper Tool Demo Track Davit Abrahamyan University of British Columbia, Fatemeh Hendijani Fard University of British Columbia | ||