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.Conference Object Citation - Scopus: 19Thquad: 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 IEEEArticle Citation - WoS: 4Citation - Scopus: 4Automating 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.Article Citation - WoS: 1Citation - Scopus: 2Information Retrieval-Based Bug Localization Approach With Adaptive Attribute Weighting(TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2021) ErşahIn, Mustafa; Utku, Semih; Kılınç, Deniz; ErşahIn, BuketSoftware quality assurance is one of the crucial factors for the success of software projects. Bug fixing has an essential role in software quality assurance, and bug localization (BL) is the first step of this process. BL is difficult and time-consuming since the developers should understand the flow, coding structure, and the logic of the program. Information retrieval-based bug localization (IRBL) uses the information of bug reports and source code to locate the section of code in which the bug occurs. It is difficult to apply other tools because of the diversity of software development languages, design patterns, and development standards. The aim of this study is to build an adaptive IRBL tool and make it usable by more companies. BugSTAiR solves the aforementioned problem by means of the adaptive attribute weighting (AAW) algorithm and is evaluated on four open-source projects which are well-known benchmark datasets on BL. One of them is BLIA which is the state of the art in bug localization area and another is BLUIR which is a well-known BL tool. According to the promising results of experiments, Top1 rank of BugSTAiR is 2% and MAP is 10% better than BLIA's results on AspectJ and it has localized 4.6% of all bugs in Top1 and its precision is 6.1% better than BLIA on SWT, respectively. On the other side, it is 20% better in the Top1 metric and 30% in precision than BLUIR.