An LLM code optimizer using control-flow-graph differences and retrieved examples reports 7.3% average runtime reduction on 116 C++ programs versus zero-shot GPT-4o.
Utilizing Deep Learning to Optimize Software Development Processes
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abstract
This study explores the application of deep learning technologies in software development processes, particularly in automating code reviews, error prediction, and test generation to enhance code quality and development efficiency. Through a series of empirical studies, experimental groups using deep learning tools and control groups using traditional methods were compared in terms of code error rates and project completion times. The results demonstrated significant improvements in the experimental group, validating the effectiveness of deep learning technologies. The research also discusses potential optimization points, methodologies, and technical challenges of deep learning in software development, as well as how to integrate these technologies into existing software development workflows.
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Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation
An LLM code optimizer using control-flow-graph differences and retrieved examples reports 7.3% average runtime reduction on 116 C++ programs versus zero-shot GPT-4o.