Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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.Article Citation - WoS: 28Citation - Scopus: 31Benchmarking the Knowledge Management Practices of Construction Firms(Vilnius Gediminas Technical University, 2012) Kale, Serdar; Karaman, Erkan A.Knowledge management is rapidly becoming a key organizational capability for creating competitive advantage in the construction industry. The emergence of knowledge management in this capacity poses enormous challenges to executives of construction firms. This paper proposes a model for benchmarking those knowledge management practices of AEC firms that can guide and assist construction business executives in meeting these challenges. The proposed model incorporates benchmarking and knowledge management concepts with importance-performance analysis (IPA) maps. It is a simple visual tool that can provide powerful diagnostic information to executives of AEC firms by evaluating their firm's knowledge management practices, identifying their firm's comparative advantages and disadvantages with regard to each knowledge management practice, and setting priorities for managerial actions related to knowledge management practices that need improvement. A real-world case study is presented to illustrate the implementation and utility of the proposed model.