A Pragmatic Framework for Component (Source Code) Retrieval

Abstract

In software development, the availability of useful and adaptable programming components or source codes is crucial. Traditional information retrieval techniques fall short in code search, as these require bridging the semantic gap between source code and natural language based queries for search. This dissertation tackles the challenge of code search in software development by offering a code retrieval framework that offers solutions based on ontologies, machine learning, and deep learning techniques. The proposed framework uses ontologies for source code search, a machine learning-based ranking schema, and advanced methods such as graph neural networks and Bi-LSTM-based neural attention. The evaluation results demonstrates the effectiveness of our approach through extensive experimentation with benchmark datasets to produce improved performance compared to existing methods. Based on our results, we can claim that software developers who want to speed up development and reduce the development cost can use the proposed framework.