WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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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.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.Article Citation - WoS: 5Citation - Scopus: 7Public Personnel Management Process Capability Assessment(SAGE Publications, 2020) Gökalp, Ebru; Demirörs, Onur; Eren, P. ErhanPersonnel management plays a critical role in the success of public organizations. Our literature review shows that there is a lack of systematic guidance on how to improve Public Personnel Management Process (PPMP) quality. Software Process Improvement and Capability Determination (SPICE) is a process assessment framework that is successfully used by software organizations during the past two decades. The framework can also be used as a baseline to generate process capability models for different specific domains/sectors. We have utilized this approach for the government domain and we developed the process definition of PPMP. To observe the benefits and usability of the model, we have performed a multiple case study, including the assessments of three organizations' PPMP capability levels and the development of action plans for PPMP improvement. The findings show that the proposed approach is applicable for identifying the PPMP capability levels and is capable of providing a roadmap for moving to the next level.Article Citation - WoS: 9A Fuzzy Logic Model for Benchmarking the Knowledge Management Performance of Construction Firms(NRC Research Press, 2011) 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 the knowledge management performance of construction firms that can guide and assist construction business executives in meeting these challenges. The proposed model incorporates benchmarking and knowledge management concepts with fuzzy set theory to adequately handle imprecision, vagueness, and uncertainty that prevail in this process. It uses the fuzzy-weighted average (FWA) algorithm to evaluate the knowledge management performance of construction firms. It is an internal reporting model that can provide powerful diagnostic information to executives of construction firms by evaluating their firm's knowledge management performance, identifying their firm's strengths and weaknesses 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.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.Article Citation - WoS: 13Citation - Scopus: 25A Diagnostic Model for Assessing the Knowledge Management Practices of Construction Firms(Korean Society of Civil Engineers, 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 as a key organizational capability for creating competitive advantage poses enormous challenges to executives of construction firms. This paper proposes a model for benchmarking the knowledge management practices of construction firms that can guide and assist construction business executives to meet these challenges. The proposed model incorporates benchmarking and knowledge management concepts with Importance-Performance Analysis (IPA) and Comparative Performance Analysis (CPA) maps. The IPA and CPA maps are visual management tools that have been commonly used for continuous improvements in processes and the performance of firms. Yet they have not been used in the construction management literature or for evaluating the knowledge management practices. The proposed model can be used by construction firms as an internal performance measurement tool to evaluate their knowledge management practices. It can provide powerful diagnostic information to construction business executives of construction firms in order to evaluate their firm's knowledge management practices, identify their firm's comparative advantages and disadvantages with regard to each knowledge management practice, and set priorities for managerial actions related to knowledge management practices that need improvement. A real-world case study was conducted by administering a survey to 105 construction firms operating in Turkey and is presented to illustrate the implementation and utility of the proposed model.