A Scalable System for Software Repository Analysis and Retrieva

The rapid growth of modern software ecosystems has resulted in massive, globally distributed code repositories, creating significant challenges in efficient indexing, retrieval, and structural analysis. Existing repository mining tools often struggle to scale, lack deep code structure correlation, or provide limited support for multi-language analysis. This paper introduces SearchSECO, a distributed, language-agnostic search and analysis engine designed for large-scale software repository mining. The system integrates a modular architecture comprising a high-throughput crawler for metadata harvesting, a parallel retriever for repository acquisition, and hybrid parsers leveraging both srcML and custom ANTLR grammars for precise method-level extraction. A distributed Apache Cassandra backend ensures scalable, fault tolerant storage, while a high-performance networking layer enables low-latency client-server communication. Experimental evaluations on diverse open-source datasets demonstrate the system’s ability to process millions of methods across thousands of repositories with near-linear scalability. By linking methods, authors, and version histories across projects, SearchSECO enables advanced cross-repository analytics for vulnerability detection, clone identification, and software evolution studies. This work contributes to the fields of software engineering and repository mining by delivering an extensible framework that combines scal- ability, accuracy, and adaptability to emerging programming languages and repository platforms.