Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

Browse

Filters

2023 - 20267

Settings

Start date: 2023Subject: search.filters.subject.Software Size Measurement

Search Results

Now showing 1 - 7 of 7
  • Article
    Automating Software Size Measurement from Python Code Using Language Models
    (Springer, 2025) Tenekeci, Samet; Unlu, Huseyin; Gul, Bedir Arda; Keles, Damla; Kuuk, Murat; Demirors, Onur
    Software size is a key input for project planning, effort estimation, and productivity analysis. While pre-trained language models have shown promise in deriving functional size from natural-language requirements, measuring size directly from source code remains under-explored. Yet, code-based size measurement is critical in modern workflows where requirement documents are often incomplete or unavailable, especially in Agile development environments. This exploratory study investigates the use of CodeBERT, a pre-trained bimodal transformer model, for measuring software size directly from Python source code according to two measurement methods: COSMIC Function Points and MicroM. We construct two curated datasets from the Python subset of the CodeSearchNet corpus, and manually annotate each function with its corresponding size. Our experimental results show that CodeBERT can successfully measure COSMIC data movements with up to 91.4% accuracy and generalize to the functional, architectural, and algorithmic event types defined in MicroM, reaching up to 81.5% accuracy. These findings highlight the potential of code-based language models for automated functional size measurement when requirement artifacts are absent or unreliable.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Automating Software Size Measurement With Language Models: Insights From Industrial Case Studies
    (Elsevier Science Inc, 2026) Unlu, Huseyin; Tenekeci, Samet; Kennouche, Dhia Eddine; Demirors, Onur
    Objective software size measurement is critical for accurate effort estimation, yet many organizations avoid it due to high costs, required expertise, and time-consuming manual effort. This often leads to vague predictions, poor planning, and project overruns. To address this challenge, we investigate the use of pre-trained language models - BERT and SE-BERT - to automate size measurement based on textual requirements using COSMIC and MicroM methods. We constructed one heterogeneous dataset and two industrial datasets, each manually measured by experienced analysts. Models were evaluated in three settings: (i) generic model evaluation, where the models are trained and tested on heterogeneous data, (ii) internal evaluation, where the models are trained and tested on organization-specific data, and (iii) external evaluation, where generic models were tested on organization-specific data. Results show that organization-specific models significantly outperform generic models, indicating that aligning training data with the target organization's requirement style is critical for accuracy. SE-BERT, a domain-adapted variant of BERT, improves performance, particularly in low-resource settings. These findings highlight the practical potential of tailoring training data for broader adoption and cost-effective software size measurement in industrial contexts.
  • Conference Object
    Measuring the Size of Change Requests in Microservice-Based Software Projects
    (Springer Science and Business Media Deutschland GmbH, 2026) Yenel, M.; Ünlu, H.; Demirors, O.
    Accurately estimating the effort required for implementing change requests remains a critical challenge in software engineering, especially in microservice-based software architectures (MSSA). Traditional functional size measurement methods often fail to capture the distinct characteristics of MSSAs. To address this limitation, we propose a change size measurement method based on MicroM, a size measurement approach specifically developed for MSSAs. The proposed method counts added, deleted, and modified events across functional, architectural, and algorithmic levels, and includes the number of affected initial requirements. We conducted an exploratory case study with 18 change requests and built four regression-based effort estimation models. The results show that combining event counts with the number of affected requirements improves estimation accuracy. Our method provides a more precise and context-aware way to estimate change-related effort in MSSA projects. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    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.
  • 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
    Citation - Scopus: 3
    Predicting Software Size and Effort From Code Using Natural Language Processing
    (CEUR-WS, 2024) Tenekeci, S.; Demirörs, Onur; Ünlü, H.; Dikenelli, E.; Selçuk, U.; Kılınç Soylu, G.; Demirörs, O.
    Software Size Measurement (SSM) holds a crucial role in software project management by facilitating the acquisition of software size, which serves as the primary input for development effort and schedule estimation. However, many small and medium-sized companies encounter challenges in conducting objective SSM and Software Effort Estimation (SEE) due to resource constraints and a lack of expert workforce. This often leads to inaccurate estimates and projects exceeding planned time and budget. Hence, organizations need to perform objective SSM and SEE with minimal resources and without relying on an expert workforce. In this research, we introduce two exploratory case studies aimed at predicting the functional size (COSMIC and Event-based size) and effort of software projects from the code using a deep-learning-based NLP model: CodeBERT. For this purpose, we collected and annotated two datasets consisting of 4800 Python and 1100 C# functions. Then, we trained a classification model to predict COSMIC data movements (entry, exit, read, write) and four regression models to predict Event-based size (interaction, communication, process) and effort. Despite utilizing a relatively small dataset for model training, we achieved promising results with an 84.5% accuracy for the COSMIC size, 0.13 normalized mean absolute error (NMAE) for the Event-based size, and 0.18 NMAE for the effort. These findings are particularly insightful as they demonstrate the practical utility of language models in SSM and SEE. © 2024 Copyright for this paper by its authors.
  • Conference Object
    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.