Merging Distinct Sources Databases to Improve Software Estimation Models

Context. For more than six decades, software cost/effort estimation has been a relevant topic for research due to its impact on the industry. Although many estimation models exist, regression-based estimation approaches have been predominantly used in the literature. However, some problems have been observed both in industry and academia: the lack of datasets with a high or at least enough number of data points and the arbitrary combination of different source databases belonging to practitioners in order to create larger datasets. Objective. Propose the application of the Kruskal–Wallis test to validate the integration of distinct source databases (independent groups), thereby avoiding the mixing of unrelated data, increasing the number of data points, and improving the estimation models. Method.We conducted a case study using real data from an international company, specifically data from their Mexico office. This office provides software development services for a technological tower identified as “Microservices and APIs.” The data were collected in 2020. Results: The quality criteria in the final estimation model were improved. The MMRE was reduced by 25.4% (from 78.6 to 53.2%), the standard deviation was reduced by 97.2% (from 149.7 to 52.5%), and the Pred (25%) indicator increased by 3.2 percentage points. Additionally, the number of data points increased significantly, and linear regression constraints was accomplished. The application of the Kruskal–Wallis test to validate the integration of distinct source databases (independent groups) proved useful in improving the estimation models.