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COAR Access Rights: metadata only accessSubject: Software Size Measurement

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    Exploratory Review of Quantum Computing Software Requirements Specification and Their Measurement
    (CEUR-WS, 2024) Hacaloglu, T.; Soubra, H.; Bourque, P.
    Quantum software sets itself apart from classical software owing to its powerful computational abilities rooted in entanglement and superposition. Unlike classical software, quantum software diverges notably across various dimensions, including computational models, hardware architectures, algorithms, deployment platforms, and problem domains. Quantum software is also often not standalone and interacts heavily with classical software, stressing the importance of carefully considering hybridization. From a software engineering standpoint, researchers generally agree that a different approach is required for quantum software, and they advocate a Quantum Software Development Life Cycle (SDLC). This exploratory study briefly outlines the specifics of quantum software, overviews the proposed approaches regarding the software requirements of quantum software, and then reviews the current alternatives for measuring the functional size of quantum software. This study indicates that only a few papers in the literature discuss the requirements and functional size measurements of quantum software. Their results are also mostly conceptual and have not yet been empirically validated. Functional size measurement using quantum software remains an open area for further research. © 2024 Copyright for this paper by its authors.
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    Citation - Scopus: 8
    An Exploratory Case Study on Effort Estimation in Microservices
    (Institute of Electrical and Electronics Engineers Inc., 2023) Unlu,H.; Hacaloglu,T.; Omural,N.K.; Caliskanel,N.; Leblebici,O.; Demirors,O.
    Software project management plays an important role in producing high-quality software, and effort estimation can be considered as a backbone for successful project management. Size is a very significant attribute of software by being the only input to perform early effort estimation. Even though functional size measurement methods showed successful results in effort estimation of traditional data-centric architectures such as monoliths, they were not designed for today's architectures which are more service-based and decentralized such as microservices. In these new systems, the event concept is highly used specifically for communication among different services. By being motivated by this fact, in this study, we looked for more microservice-compatible ways of sizing microservices using events and developed a method accordingly. Then, we conducted an exploratory case study in an organization using agile methods and measured the size of 17 Product Backlog Items (PBIs) to assess how this proposed method can be useful in effort estimation in microservices. The implication from the case study is that despite performing a more accurate effort estimation using the proposed size measurement than COSMIC, we were unable to significantly outperform using the total number of events. However, our suggested approach demonstrated to us a different way to use software size in terms of events, namely, to determine the coupling complexity of the project. This finding can be beneficial specifically when evaluating the change requests. © 2023 IEEE.
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    Citation - WoS: 3
    Citation - Scopus: 5
    Predicting 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, Onur
    Software 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.