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: 3Citation - Scopus: 5Predicting Software Functional Size Using Natural Language Processing: an Exploratory Case Study(IEEE, 2024) Unlu, Huseyin; Tenekeci, Samet; Ciftci, Can; Oral, Ibrahim Baran; Atalay, Tunahan; Hacaloglu, Tuna; Demirors, OnurSoftware Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and Software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.Conference Object Citation - WoS: 1Citation - Scopus: 1Towards the Construction of a Software Benchmarking Dataset Via Systematic Literature Review(IEEE, 2024) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, OnurEffort 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 Citation - WoS: 2Citation - Scopus: 3Impact of Variations in Synthetic Training Data on Fingerprint Classification(IEEE, 2019) İrtem, Pelin; İrtem, Emre; Erdoğmuş, NesliCreating and labeling data can be extremely time consuming and labor intensive. For this reason, lack of sufficiently large datasets for training deep structures is often noted as a major obstacle and instead, synthetic data generation is proposed. With their high acquisition and labeling complexity, this also applies to fingerprints. In the literature, a number of synthetic fingerprint generation systems have been proposed, but mostly for algorithm evaluation purposes. In this paper, we aim to analyze the use of synthetic fingerprint data with different levels of degradation for training deep neural networks. Fingerprint classification problem is selected as a case-study and the experiments are conducted on a public domain database, NIST SD4. A positive correlation between the synthetic data variation and the classification rate is observed while achieving state-of-the-art results.Conference Object Citation - WoS: 14Citation - Scopus: 14An Overview of the Recent Advances in Fbg-Assisted Phase-Sensitive Otdr Technique and Its Applications(IEEE, 2020) Yüksel, Kıvılcım; Koçal, Ertunga Burak; Jason, Johan; Wuilpart, Marc; Sainz, Manuel Lopez-AmoIn this paper, we discuss the operation principles, sensing mechanism, challenges and application areas of FBG-assisted phase-sensitive optical time-domain reflectometry. A special emphasis is given to the interrogation of fiber Bragg grating arrays for vibration sensing application. Results obtained by different research groups are compared in terms of performance characteristics and future perspectives. Recent progress obtained through our research collaboration are also presented. In particular, the detrimental spectral shadowing effect and multiple reflection crosstalk are analysed and mitigation techniques are proposed. © 2020 IEEE.Conference Object Citation - WoS: 3Citation - Scopus: 6Spl-At Gherkin: a Gherkin Extension for Feature Oriented Testing of Software Product Lines(IEEE, 2019) Tuğlular, Tuğkan; Şensülün, SecanAs cloud platforms turn into software product lines (SPLs), testing products composed of customer selected features becomes more and more important. In this paper, we propose a feature-oriented testing approach for platform-based SPLs through a novel extension to Gherkin called SPL-AT Gherkin and a novel automatic test method generation technique, which utilizes TestNG framework. We demonstrate the applicability of the proposed approach by a case study.Conference Object Citation - WoS: 5Citation - Scopus: 5Featured Event Sequence Graphs for Model-Based Incremental Testing of Software Product Lines(IEEE, 2019) Tuğlular, Tuğkan; Beyazıt, Mutlu; Öztürk, DilekThe goal of software product lines (SPLs) is rapid development of high-quality software products in a specific domain with cost minimization. To assure quality of software products from SPLs, products need to be tested systematically. However, testing every product variant in isolation is generally not feasible for large number of product variants. An approach to deal with this issue is to use incremental testing, where test artifacts that are developed for one product are reused for another product which can be obtained by incrementally adding features to the prior product. We propose a novel model-based test generation approach for products developed using SPL that follows incremental testing paradigm. First, we introduce Featured Event Sequence Graphs (FESGs), an extension of ESGs, that provide necessary definitions and operations to support commonalities and variabilities in SPLs with respect to test models. Then we propose a test generation technique for the product variants of an SPL, which starts from any product. The proposed technique with FESGs avoids redundant test generation for each product from SPL. We compare our technique with in-isolation testing approach by a case study.