ABSTRACT Methodology In this research, we conducted a case study to establish a foundation for size measurement and effort estimation in DWH projects. We first applied a productivity‐based estimation approach using linear regression with the ISBSG repository to assist organizations without historical data. We then evaluated various machine learning algorithms to improve estimation accuracy. Finally, we tested a combined model that integrates both approaches for estimating effort in external projects. Results Using the ISBSG dataset, linear regression models based on productivity achieved a Mean Magnitude of Relative Error (MMRE) of 0.285. Machine learning algorithms improved accuracy by 22.81%, reducing the MMRE to 0.220. The final model, applied to external projects, yielded MRE values between 0.010 and 0.245. Conclusion The ISBSG repository is a valuable resource for effort estimation in DWH projects. Combining productivity‐based estimation with machine learning enhances accuracy and predictive performance, making it a more reliable approach than traditional models.

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.

Agile software development methodologies have been widely adopted by the software industry during the last decade. Agility assessment is an approach to measure the success of this adoption as well as to satisfy the further demands. In response, a number of agile maturity self-assessment surveys have been developed. However, software organizations do not widely utilize existing self-assessment surveys. In this study we aim to identify the existing surveys and evaluate their strengths and weaknesses in agility assessment. Firstly, an exploratory case study is performed by using one of the surveys in order to determine the expected features of an agile maturity self-assessment survey. Then, a comprehensive case study is conducted to measure the sufficiency of the mostly referenced agile maturity self-assessment surveys according to the determined features. The study results reveal that current surveys do not meet the expected features comprehensively.