Adnan Ahmed Khan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Adnan Ahmed Khan has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Ayesha Maqbool
Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
1 shared publication - Farkhanda Afzal
School of Electrical Engineering and Computer Sciences, National University of Sciences and Technology (NUST), Islamabad, Pakistan
1 shared publication - Nazia Bibi
Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan
1 shared publication - Tauseef Rana
Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
1 shared publication
Latest Publications
- Open Access
- 2 July 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (13) , 5795
C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
To enhance the software implementation process, developers frequently leverage preexisting code snippets by exploring an extensive codebase. Existing code search tools often rely on keyword- or syntactic-based methods and struggle to fully grasp the semantics and intent behind code snippets. In this paper, we propose a novel hybrid C2B model that combines CodeT5 and bidirectional long short-term memory (Bi-LSTM) for source code search and recommendation. Our proposed C2B hybrid model leverages CodeT5’s domain-specific pretraining and Bi-LSTM’s contextual understanding to improve code representation and capture sequential dependencies. As a proof-of-concept application, we implemented the proposed C2B hybrid model as a deep neural code search tool and empirically evaluated the model on the large-scale dataset of CodeSearchNet. The experimental findings showcase that our methodology proficiently retrieves pertinent code snippets and surpasses the performance of prior state-of-the-art techniques.
- 2 July 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (13) , 5795
Open AccessC2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
To enhance the software implementation process, developers frequently leverage preexisting code snippets by exploring an extensive codebase. Existing code search tools often rely on keyword- or syntactic-based methods and struggle to fully grasp the semantics and intent behind code snippets. In this paper, we propose a novel hybrid C2B model that combines CodeT5 and bidirectional long short-term memory (Bi-LSTM) for source code search and recommendation. Our proposed C2B hybrid model leverages CodeT5’s domain-specific pretraining and Bi-LSTM’s contextual understanding to improve code representation and capture sequential dependencies. As a proof-of-concept application, we implemented the proposed C2B hybrid model as a deep neural code search tool and empirically evaluated the model on the large-scale dataset of CodeSearchNet. The experimental findings showcase that our methodology proficiently retrieves pertinent code snippets and surpasses the performance of prior state-of-the-art techniques.