MicroRec: Leveraging Large Language Models for Microservice Recommendation

• Published in: Proceedings (IEEE/ACM International Conference on Mining Software Repositories. Online) pp. 419 - 430
• Main Authors: Alsayed, Ahmed Saeed, Khanh Dam, Hoa, Nguyen, Chau
• Format: Conference Proceeding
• Language: English
• Published: ACM 15.04.2024
• Subjects: Accuracy; Data mining; Docker Hub; Large Language Models; Measurement; Microservice architectures; Microservices; Natural language processing; Recommendation System; Semantic Search; Semantics; Software
• ISSN: 2574-3864
• DOI: 10.1145/3643991.3644916

The increasing adoption of microservices in software development requires effective recommendation systems that guide developers to relevant microservices. In this paper, we introduce MicroRec, a novel microservice recommender framework which leverages insights from Stack Overflow posts and the power of Large Language Models (LLMs). MicroRec utilizes a dual-encoder architecture that combines contrastive learning and semantic similarity learning, allowing us to achieve robust and accurate retrieval and ranking of relevant posts based on user queries. Using LLMs, MicroRec builds up a deep understanding of both user queries and microservices through the information they provide (e.g., README files and Dockerfiles). Our empirical evaluations demonstrate significant improvements brought by MicroRec over the existing methods across a variety of performance metrics including MRR, MAP, and precision@k. In addition, the results returned by MicroRec were fourteen times more accurate than those provided by the existing recommendation tool on the widely-used Docker Hub platform.CCS CONCEPTS* Computing methodologies → Semantic networks; Natural language processing; * Applied computing → Document searching; * Information systems → Recommender systems.