Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Correction Automating Software Size Measurement From Python Code Using Language Models (Vol 33, 19, 2026)(Springer, 2025) Tenekeci, Samet; Unlu, Huseyin; Gul, Bedir Arda; Keles, Damla; Kucuk, Murat; Demirors, OnurArticle Automating Software Size Measurement from Python Code Using Language Models(Springer, 2025) Tenekeci, Samet; Unlu, Huseyin; Gul, Bedir Arda; Keles, Damla; Kuuk, Murat; Demirors, OnurSoftware 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: 1Citation - Scopus: 1Automating Software Size Measurement With Language Models: Insights From Industrial Case Studies(Elsevier Science Inc, 2026) Unlu, Huseyin; Tenekeci, Samet; Kennouche, Dhia Eddine; Demirors, OnurObjective 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.Article An Alternative Software Benchmarking Dataset: Effort Estimation With Machine Learning(Elsevier Science Inc, 2026) Yurum, Ozan Rasit; Unlu, Huseyin; Demirors, OnurEffort estimation plays a vital role in software project planning, as accurate estimates of required human resources are essential for success. Traditional estimation models often depend on historical size and effort data, yet organizations frequently struggle to access reliable effort records. Public benchmarking datasets like ISBSG offer useful data but may lack coverage or involve licensing fees. To address this issue, we previously introduced a free, extendable benchmarking dataset that integrates functional size and effort data extracted from 18 studies. In this study, we examine the effectiveness of our dataset for predictive effort estimation and compare it with the widely used ISBSG dataset. Our analysis includes 337 records from our dataset and 732 ISBSG projects, focusing on those with COSMIC size data. We first developed and compared models using linear regression and nine machine learning algorithms - Bayesian Ridge, Ridge Regression, Decision Tree, Random Forest, XGBoost, LightGBM, k-Nearest Neighbors, Multi-Layer Perceptron, and Support Vector Regression. Then, we selected the best-performing models and applied them to an unseen evaluation dataset to assess their generalization performance. The results show that machine learning performance varies based on evaluation method and dataset characteristics. Despite having fewer records, our dataset enabled more accurate predictions than ISBSG in most cases, highlighting its potential for effort estimation. This study demonstrates the viability of our dataset for building predictive models and supports the use of machine learning in improving estimation accuracy. Expanding this dataset could offer a valuable, open-access resource for organizations seeking effective and lowcost estimation solutions.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 Analysis, Design, Test, and Devops in Microservice-Based Software Architectures: Results From Pakistan(Springer international Publishing Ag, 2024) Unlu, Huseyin; Soylu, Gorkem Kilinc; Ahmad, Isra Shafique; Demirors, OnurIn today's software industry, Microservice-based Software Architecture (MSSA) has been a common practice and has been adopted by many companies. MSSA differs from traditional object-oriented architecture in several ways. The architecture moved away from being data-driven and evolved into a behavior-oriented structure. The usage of a single database is replaced by the structures in which each microservice is developed independently and has its own database. Therefore, adaptation demands software organizations to transform their culture. However, there is no de facto method for analyzing, designing, and testing systems for these architectures, similar to object-oriented analysis and design practices. This study aimed to understand how Pakistani software organizations undertake analysis, design, test, and DevOps processes in software projects adopting the MSSA paradigm. To achieve this goal, we surveyed 49 participants from various agile organizations in Pakistan, encompassing different roles and domains. The results reveal that Pakistani software organizations continue using familiar object-oriented analysis and design approaches. However, they have already started exploring event-oriented analysis and design methods for MSSA projects.Article Citation - WoS: 9Citation - Scopus: 13Microservice-Based Projects in Agile World: a Structured Interview(Elsevier, 2024) Unlu, Huseyin; Kennouche, Dhia Eddine; Soylu, Gorkem Kiling; Demirors, OnurContext: During the last decade, Microservice-based software architecture (MSSA) has been a preferred design paradigm for a growing number of companies. MSSA, specifically in the form of reactive systems, has substantial differences from the more conventional design paradigms, such as object-oriented analysis and design. Therefore, adaptation demands software organizations to transform their culture. However, there is a lack of research studies that explore common practices utilized by software companies that implement MSSAs.Objective: In this study, our goal is to get an insight into how practices such as an agile methodology, software analysis, design, test, size measurement, and effort estimation are performed in software projects which embrace the Microservice-based software architecture paradigm. Together with the identification of practices utilized for the MSSA paradigm, we aim to determine the challenges organizations face to adopt microservice-based software architectures.Method: We performed a structured interview with participants coming from 20 different organizations over different roles, domains, and countries to collect information on their views, experience, and the challenges faced.Results: Our results reveal that organizations find agile development compatible with microservices. In general, they continue to use traditional object-oriented modeling notations for analysis and design in an abstract way. They continue to use the same subjective size measurement and effort estimation approaches that they were using previously in traditional architectures. However, they face unique challenges in developing microservices.Conclusion: Although organizations face challenges, practitioners continue to use familiar techniques that they have been using for traditional architectures. The results provide a snapshot of the software industry that utilizes microservices.Conference Object Citation - WoS: 2Citation - Scopus: 2Effort Prediction With Limited Data: a Case Study for Data Warehouse Projects(IEEE, 2022) Unlu, Huseyin; Yildiz, Ali; Demirors, OnurOrganizations may create a sustainable competitive advantage against competitors by using data warehouse systems with which they can assess the current status of their operations at any moment. They can analyze trends and connections using up-to-date data. However, data warehouse projects tend to fail more often than other projects as it can be tough to estimate the effort required to build a data warehouse system. Functional size measurement is one of the methods used as an input for estimating the amount of work in a software project. In this study, we formed a measurement basis for DWH projects in an organization based on the COSMIC Functional Size Measurement Method. We mapped COSMIC rules on two different architectures used for DWH projects in the organization and measured the size of the projects. We calculated the productivity of the projects and compared them with the organization's previous projects and DWH projects in the ISBSG repository. We could not create an organization-wide effort estimation model as we had a limited number of projects. As an alternative, we evaluated the success of effort estimation using DWH projects in the ISBSG repository. We also reported the challenges we faced during the size measurement process.Conference Object Citation - WoS: 7Citation - Scopus: 12Utilization of Three Software Size Measures for Effort Estimation in Agile World: a Case Study(IEEE, 2022) Unlu, Huseyin; Hacaloglu, Tuna; Buber, Fatma; Berrak, Kivilcim; Leblebici, Onur; Demirors, OnurFunctional size measurement (FSM) methods, by being systematic and repeatable, are beneficial in the early phases of the software life cycle for core project management activities such as effort, cost, and schedule estimation. However, in agile projects, requirements are kept minimal in the early phases and are detailed over time as the project progresses. This situation makes it challenging to identify measurement components of FSM methods from requirements in the early phases, hence complicates applying FSM in agile projects. In addition, the existing FSM methods are not fully compatible with today's architectural styles, which are evolving into event-driven decentralized structures. In this study, we present the results of a case study to compare the effectiveness of different size measures: functional -COSMIC Function Points (CFP)-, event-based - Event Points-, and code length-based - Line of Code (LOC)- on projects that were developed with agile methods and utilized a microservice- based architecture. For this purpose, we measured the size of the project and created effort estimation models based on three methods. It is found that the event-based method estimated effort with better accuracy than the CFP and LOC-based methods.