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 - WoS: 1
    Citation - Scopus: 1
    Towards the Construction of a Software Benchmarking Dataset Via Systematic Literature Review
    (IEEE, 2024) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, Onur
    Effort estimation is a fundamental task during the planning of software projects. Prediction models usually rely on two essential factors: software size and effort data. Measuring the size of the software can be done at various stages of the project with desired accuracy. Nevertheless, the industry faces challenges when it comes to collecting reliable actual effort data. Consequently, organizations encounter difficulties in establishing effort prediction models. Benchmarking datasets are available, but, in most cases, they have huge variances that make them less useful for effort prediction. In this study, we aimed to answer whether creating a software benchmarking dataset is possible by gathering the data from the literature. To the best of our knowledge, a comprehensive dataset that gathers the functional size and effort data of the studies from the literature is unavailable. For this purpose, we performed a systematic literature review to find studies that include projects measured with the COSMIC Functional Size Measurement (FSM) method and the related effort. As a result, we formed a dataset including 337 records from 18 studies that shared the corresponding size and effort data. Although we performed a limited search, we created a larger dataset than many datasets in the literature. In light of our review, we obtained that most studies did not share their dataset, and many lacked case details such as implementation environment and the scope of software development life cycle activities included in the effort data. We also compared the dataset with the ISBSG repository and found that our dataset has less variation in productivity. Our review showed the applicability of creating a software benchmarking dataset is possible by gathering the data from the literature. In conclusion, this study addresses gaps in the literature through a cost-free and easily extendable dataset.
  • Conference Object
    Doğruluk Problemi için Veri Kümesi Hazırlanması
    (CEUR Workshop Proceedings, 2018) Karabayır, Arif Kürşat; Tek, Ozan Onur; Çınar, Özgür Fırat; Tekir, Selma
    Internet has become one of the most important information sources. With the advent of Internet, the ease of access and sharing of information have caused the emergence of conflicting information. The increase in conflicting information makes it a challenge to find the truth out of it. This problem is named as the veracity problem. The algorithms that were developed in response to this problem accept structured data as in¬ put. Thus, to be able to use these algorithms on Internet, there is a need to transform the unstructured data on the Internet into a structured form. This need is hard to fulfill in a domain-independent and automatic way considering the variety on Internet. In this work; structured data preparation to test the effectiveness of the truth-finder algorithms is experienced. The process of transforming the unstructured data on the Internet into a structured form is described in steps to contribute its generalization in a domain-independent way. As a result of this process, a new quotes data set is constructed and a truth-finder algorithm is tested on this dataset by giving some comments on it.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    Türkçe Manzara Metni Veri Kümesi
    (IEEE, 2017) Erdogmus, Nesli
    Scene text localization and recognition keeps attracting an increasing interest from researchers due to its valuable advantage in extracting content from real world images and in image retrieval via text search. Nevertheless, due to the fact that the majority of the image datasets that are commonly used in this field is comprised of text in English, the related studies have mostly been limited to a single language. On that account, in order to apply the technologies developed for scene text detection and recognition to Turkish scene text, analyze their performances and to develop Turkish language specific algorithms, a Turkish scene text database is collected for the first time in the literature. In this paper, the contents of this database, shortly called STRIT (Scene Text Recognition In Turkish), are detailed. Additionally, two baseline methods are tested to detect and recognize scene text in Turkish and the preliminary results are presented.