Ho Le Thi Kim Nhung

Effective software effort estimation is essential for project management, but faces scalability challenges with large datasets. While clustering can address this complexity, standard methods often rely on random initial centers, leading to inconsistent and less precise results. This randomness frequently overlooks critical contextual factors, such as industry or domain-specific characteristics, which can impair cluster quality and the accuracy of effort predictions. To overcome these issues, this study introduces the Contextual Initial Cluster Centroids (CICC), a novel methodology designed to optimize initial centroid selection. Unlike approaches that depend on randomness or are computationally intensive, CICC uses parallel processing of Jaccard similarity and a refined neighbor-finding technique, K-Reciprocal Nearest Neighbors (KRNN), to identify the most relevant and similar projects as initial centers. This deterministic approach ensures clusters are built around meaningful, context-rich representatives, reducing computations and improving scalability. Experiments on public software project datasets show that CICC significantly outperforms existing techniques. It achieves higher cluster quality, measured by metrics like the Global Silhouette Index, and provides more accurate effort estimates, indicated by lower MAE and higher PRED values. By establishing a more robust and efficient foundation for effort estimation, CICC offers considerable potential to optimize project planning and resource allocation in large-scale software development.

Introduction: The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group’s Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. Aim: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. Method: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. Results: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. Conclusion: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sector is the most suitable clustering method among the tested clustering methods.

Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.