Optimising productivity and automating software development: Innovative memory system approaches in large language models

Abstract

This study explored innovative approaches to enhancing memory systems in large language models to improve efficiency and automate software development. The primary focus was on optimising memory systems that enable long-term context storage and facilitate model adaptation to evolving interaction conditions. The research analysed contemporary methods of data storage and processing that enhance the ability of models to handle large volumes of information efficiently. This included the utilisation of specialised algorithms and memory mechanisms that improve the accuracy and adaptability of large language models in executing complex tasks. A secondary focus of the study examined the capabilities of large language models in automating software development. It assessed how these models can generate code, optimise it, and perform error detection. Particular attention was given to analysing the impact of automation on software quality and development time. In this context, the study investigated the use of large language models for automating repetitive tasks, generating tests, and implementing best programming practices. The findings indicated that enhancing the memory systems of large language models significantly improves their efficiency in tasks requiring long- term interaction. Integrating such models into software development processes has been shown to reduce both time and resource expenditures while enhancing product quality. The practical significance of this study lies in the formulation of recommendations for the optimal utilisation of large language models in the field of information technology