{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260100192"},"data":{"type":"grant-details","id":"DE260100192","attributes":{"code":"DE260100192","administering-organisation":"Monash University","announcement-administering-organisation":"Monash University","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-01","anticipated-end-date":"2028-12-31","grant-summary":"Energy-Efficient Models for Sustainable Intelligent Software Engineering. This project aims to tackle the high operational costs and environmental impacts associated with software engineering tools powered by large language models. The project expects to develop novel and effective techniques to reduce energy consumption when the models are deployed for service. Expected outcomes of this project include new insights in software engineering with the creation of the first compact code representations, a domain-specific model distillation framework, and an adaptive dynamic inference management strategy. This should provide significant benefits to reduce both environmental impact and operational expenses, making the techniques more accessible, sustainable, and beneficial for scholarly, public, and commercial sectors.","funding-current":533122.00,"funding-at-announcement":528893,"investigators-current":[{"title":"Dr","firstName":"Xiaoning","familyName":"Du","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0003-3728-9541 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Xiaoning","familyName":"Du","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0003-3728-9541 "}],"organisations-current":[{"organisationName":"Monash University","roleName":"Administering Organisation","state":"VIC"}],"organisations-at-announcement":[{"organisationName":"Monash University","roleName":"Administering Organisation","state":"VIC"}],"field-of-research":[{"isPrimary":true,"code":"4612","name":"Software Engineering","type":"FOR20"},{"isPrimary":false,"code":"461201","name":"Automated Software Engineering","type":"FOR20"}],"socio-economic-objective":[{"code":"220401","name":"Application Software Packages","type":"SEO20"}],"international-collaboration":[],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"Australia is undergoing a rapid digital transformation, with software engineering playing a critical role in shaping its digital infrastructure. The rise of Large Language Models (LLMs) is revolutionizing software development and maintenance, driving faster development cycles and improving software quality. Beyond software, LLM’s programming capabilities are driving the automation of complex engineering and scientific tasks in fields like medical science, chemical engineering, and robotics. To remain competitive, Australia’s research, education, and business sectors must develop, teach, and adopt AI-driven software engineering solutions at scale. With LLM-powered coding interactions set to grow exponentially, the high environmental and economic cost of LLMs remains a major barrier. While energy efficiency optimizations exist, domain-specific optimization for coding is a significant underexplored gap in the literature. This project will advance energy-efficient LLMs for code-related tasks, cutting computational costs while maintaining performance in ways that will benefit Australia economically and environmentally through more efficient LLMs. The optimized models and techniques will be promoted outside academia by being made publicly available so universities, research institutes, businesses, and nonprofits can adopt sustainable LLM-driven software engineering, fostering technological self-sufficiency and reducing reliance on costly external AI solutions."}}}