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Author: Demirors, OnurSubject: Functional Size

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    Citation - WoS: 2
    On the Seven Misconceptions About Functional Size Measurement
    (Ieee, 2016) Ozkan, Baris; Demirors, Onur
    Among the various approaches to software size measurement, Functional Size Measurement has been widely recognized for its usability in early phases of software development cycles and its independence from implementation language, development method and technology. Starting from its introduction with the original Function Point Analysis method in 1979, functional size has been a favored input to estimation and productivity models. As a result of the search for solutions to emerging measurement needs and the advancements in the discipline of software measurement, FSM concepts have been redefined and measurement methods have matured with notable contributions from the ISO standardization process. Despite the progress towards an unambiguously defined and versatile measure in software engineering, several misconceptions about FSM in software community keep on leading to misuse of functional size and unproductive measurement practices. While such misperceptions results in disappointment and wasted resources, an important consequence is the disinterest in FSM. In this paper, we elaborate seven misconceptions in FSM. We review functional size and FSM by discussing the misconceptions. Our purpose is to give a state-of-the-art presentation of functional size and to guide software practitioners and researchers in applying FSM principles properly in their practices and software engineering methods and models.
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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.