Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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  • Article
    An Information Retrieval-Based Regression Test Selection Technique
    (Springer International Publishing, 2023) Erşahin, B.; Erşahin, M.
    Regression testing (RT) is the crucial part of the software testing process. It is applied after a bug fix or a change in the functionality of the codebase. The main goal is to ensure that the modified software has the desired outcome and does not cause adverse effects in other parts of the software. RT may be costly depending on the test’s quantity and complexity. Therefore, regression test selection (RTS) can be introduced to minimize these costs. RTS runs only the test cases related to the modified parts of the software. Currently, various RTS studies focus on compiled languages such as Java, C/C++, and C#, and they mostly rely on direct code dependency between tests and the system under test. In this study, we have introduced a new RTS tool called Smartest to reduce the number of selected integration tests. Former RTS tools were focused mainly on unit tests according to dependencies of modified source files. Smartest is the first RTS tool that works for software written in JavaScript and can select integration tests written in natural language by the quality assurance team. Smartest is tested on three commercial projects and observed that it picks 13% of all test cases on average. Experiments show that Smartest minimizes the selected integration tests on RTS processes, although it does not use file-level code dependency. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.
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    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