000			01564nam a22001697a 4500
003			NUST
082			_a005.1,BIB
100			_aBibi, Nazia _94781
245			_aA Pragmatic Framework for Component (Source Code) Retieval / _cNazia Bibi
260			_aRawalpindi, _bMCS (NUST), _c2024
300			_axxii, 296 p
505			_aIn 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.
650			_aPhD Computer Software Engineering Thesis _9132801
651			_aPhD CSE Thesis _9132802
700			_aSupervised by Dr. Tauseef Ahmed Rana _9132927
942			_2ddc _cTHE
999			_c615952 _d615952