Hacaloğlu, Tuna

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https://hdl.handle.net/20.500.14411/7244
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Hacaloğlu,T.
H.,Tuna
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Hacaloglu, Tuna
Tuna, Hacaloğlu
Hacaloglu,T.
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Hacaloğlu, Tuna
Hacaloglu T.
Job Title
Doktor Öğretim Üyesi
Email Address
tuna.hacaloglu@atilim.edu.tr
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Information Systems Engineering
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56422190200
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WoS Researcher ID
KHJ-3791-2024

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IEEE Software2
CEUR Workshop Proceedings -- Joint of the 33rd International Workshop on Software Measurement and the 18th International Conference on Software Process and Product Measurement, IWSM-MENSURA 2024 -- 30 September 2024 through 4 October 2024 -- Montreal -- 2044672
18th IFIP WG 6.11Conference on e-Business, e-Services, and e-Society (I3E) -- SEP 18-20, 2019 -- Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept Comp, Trondheim, NORWAY1
45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) / 22nd Euromicro Conference on Digital System Design (DSD) -- AUG 28-30, 2019 -- Kallithea, GREECE1
48th Euromicro Conference on Software Engineering and Advanced Applications -- AUG 31-SEP 02, 2022 -- SPAIN1
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Author: Ciftci, CanSubject: Nlp

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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.