GA4GC: Greener Agent for Greener Code via Multi-Objective Configuration Optimization
Coding agents powered by Large Language Models (LLMs) face critical sustainability and scalability challenges in industrial deployment, often incurring costs that may exceed optimization benefits. We introduce GA4GC, the first framework to optimize coding agent runtime (greener agent) and code performance (greener code) trade-offs by discovering Pareto-optimal agent hyperparameters and prompt templates. Evaluation on the SWE-Perf benchmark demonstrates up to 135-fold hypervolume improvement, reducing agent runtime by 37.7% while improving correctness. Baseline comparisons and influence analysis confirm the effectiveness of GA4GC, identify temperature as the most influential hyperparameter, and provide actionable strategies to balance agent and code sustainability in industrial deployment.
Sun 16 NovDisplayed time zone: Seoul change
14:00 - 15:30 | |||
14:00 10mTalk | Challenge Overview SSBSE Challenge | ||
14:10 20mTalk | GA4GC: Greener Agent for Greener Code via Multi-Objective Configuration Optimization SSBSE Challenge Jingzhi Gong University of Leeds, Yixin Bian Harbin Normal University, Luis de la Cal Universidad Politécnica de Madrid, Giovanni Pinna University of Trieste, Anisha Uteem King's College London, David Williams University College London, Mar Zamorano López University College London, Karine Even-Mendoza King’s College London, William Langdon University College London, Hector Menendez King’s College London, Federica Sarro University College London Pre-print | ||
14:30 20mTalk | GreenMalloc: Allocator Optimisation for Industrial Workloads SSBSE Challenge Aidan Dakhama King's College London, William Langdon University College London, Hector Menendez King’s College London, Karine Even-Mendoza King’s College London Pre-print | ||
14:50 20mTalk | Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL SSBSE Challenge Ayse Irmak Ercevik King's College London, Aidan Dakhama King's College London, Melane Navaratnarajah King's College London, Yazhuo Cao King's College London, Leo Fernandes Federal Institute of Alagoas (IFAL) Pre-print File Attached | ||
15:10 20mTalk | HotCat: Green and Effective Feature Selection for HotFix Bug Taxonomy SSBSE Challenge Luis de la Cal Universidad Politécnica de Madrid, Yazhuo Cao King's College London, Ayse Irmak Ercevik King's College London, Giovanni Pinna University of Trieste, Lukas Twist King's College London, David Williams University College London, Karine Even-Mendoza King’s College London, William Langdon University College London, Hector Menendez King’s College London, Federica Sarro University College London Pre-print | ||