Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Conference Object
    Citation - Scopus: 19
    Thquad: Turkish Historic Question Answering Dataset for Reading Comprehension
    (Institute of Electrical and Electronics Engineers Inc., 2021) Soygazi,F.; Çiftçi,O.; Kök,U.; Cengiz,S.
    Question answering(QA) is a field in natural language processing and information retrieval, it aims to give answers to the questions using natural language. In this paper, we present the Turkish question answering dataset, which is THQuAD and baseline results with contextualized word embeddings. THQuAD consists of two different datasets one of them is TQuad on Turkish Islamic Science history within the scope of Teknofest 2018 "Artificial Intelligence competition", the second dataset on Ottoman history within the scope of Teknofest 2020 "Dogal Dil íçleme Yarismasi" prepared by us. THQuAD is a reading comprehension dataset, consisting of questions, answers, and passages. Our objective is to give an answer to a specific question by understanding the passage and extracting the answer from this passage. We generate contextualized word embeddings from pre-trained Turkish Bert, Electra, Albert language models after fine-tuning on different hyperparameters with neural networks. © 2021 IEEE
  • Article
    Citation - WoS: 4
    Citation - Scopus: 4
    Automating Modern Code Review Processes With Code Similarity Measurement
    (Elsevier B.V., 2024) Kartal,Y.; Akdeniz,E.K.; Özkan,K.
    Context: Modern code review is a critical component in software development processes, as it ensures security, detects errors early and improves code quality. However, manual reviews can be time-consuming and unreliable. Automated code review can address these issues. Although deep-learning methods have been used to recommend code review comments, they are expensive to train and employ. Instead, information retrieval (IR)-based methods for automatic code review are showing promising results in efficiency, effectiveness, and flexibility. Objective: Our main objective is to determine the optimal combination of the vectorization method and similarity to measure what gives the best results in an automatic code review, thereby improving the performance of IR-based methods. Method: Specifically, we investigate different vectorization methods (Word2Vec, Doc2Vec, Code2Vec, and Transformer) that differ from previous research (TF-IDF and Bag-of-Words), and similarity measures (Cosine, Euclidean, and Manhattan) to capture the semantic similarities between code texts. We evaluate the performance of these methods using standard metrics, such as Blue, Meteor, and Rouge-L, and include the run-time of the models in our results. Results: Our results demonstrate that the Transformer model outperforms the state-of-the-art method in all standard metrics and similarity measurements, achieving a 19.1% improvement in providing exact matches and a 6.2% improvement in recommending reviews closer to human reviews. Conclusion: Our findings suggest that the Transformer model is a highly effective and efficient approach for recommending code review comments that closely resemble those written by humans, providing valuable insight for developing more efficient and effective automated code review systems. © 2024 Elsevier B.V.