Yazar "Rana, Tauseef" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    An Intelligent Platform for Software Component Mining and Retrieval
    (Mdpi, 2023) Bibi, Nazia; Rana, Tauseef; Maqbool, Ayesha; Afzal, Farkhanda; Akguel, Ali; De la sen, Manuel
    The development of robotic applications necessitates the availability of useful, adaptable, and accessible programming frameworks. Robotic, IoT, and sensor-based systems open up new possibilities for the development of innovative applications, taking advantage of existing and new technologies. Despite much progress, the development of these applications remains a complex, time-consuming, and demanding activity. Development of these applications requires wide utilization of software components. In this paper, we propose a platform that efficiently searches and recommends code components for reuse. To locate and rank the source code snippets, our approach uses a machine learning approach to train the schema. Our platform uses trained schema to rank code snippets in the top k results. This platform facilitates the process of reuse by recommending suitable components for a given query. The platform provides a user-friendly interface where developers can enter queries (specifications) for code search. The evaluation shows that our platform effectively ranks the source code snippets and outperforms existing baselines. A survey is also conducted to affirm the viability of the proposed methodology.
  • [ X ]
    Öğe
    Enhancing Semantic Code Search With Deep Graph Matching
    (IEEE-Inst Electrical Electronics Engineers Inc, 2023) Bibi, Nazia; Maqbool, Ayesha; Rana, Tauseef; Afzal, Farkhanda; Akgul, Ali; Eldin, Sayed M.
    The job of discovering appropriate code snippets against a natural language query is an important task for software developers. Appropriate code retrieval increases software productivity and quality as well. In contrast to traditional information retrieval techniques, code search necessitates bridging the semantic breach between programming languages and natural language to search code fragments. Deep neural networks for search codes have recently been a hot topic in research. The standard neural code quest approaches present source code and query in the form of text as independent embedding, then calculate the semantic similarity between them using vector distance (e.g., using cosine similarity). Although recent research utilized query and code snippets during code search, it overlooked the contained rich semantic information and deep structural features between them. In this study, we are also dealing with the problem of code search by providing a deep neural solution that facilitates software developers during software development. Our proposed model effectively used neural graph matching and a searching approach for semantic code retrieval. It first converts both query and code fragments in graph format and then the semantic matching module is used to facilitate the process of matching that will retrieve the best-matched code snippets. It not only exploits the enriched semantic meanings and features, but it also uses the cross-attention mechanism to learn the fine-grained similarity that exists between query and code. The proposed model's evaluation is done using the Codesearchnet dataset with six representative programming languages. It provides comparatively good results as compared to existing baselines. It enables users to find required code snippets, and ranking is used to retrieve top 10 results. The accuracy of the proposed system is approximately 97%.