Abstract In this study, first, we improved an existing variant of the Nearest Centroid algorithm. In this new version, the predictive power of features and within-class variances are used as weights in distance calculation. This version is called the Enhanced Nearest Centroid (ENC). Second, we proposed a new model tree algorithm for binary classification. It is named as the Enhanced Nearest Centroid Model Tree (ENCMT). The model tree is built using ENC at each leaf node of the decision tree. To evaluate the performance of the new model tree, we used an independent test platform and ran the algorithm on 30 binary datasets available therein. Results showed that ENCMT improves the performance of the decision tree algorithm. We also compared ENCMT with the Logistic Model Tree (LMT) algorithm and showed that it outperforms LMT as well. We also designed a bagging algorithm where ENCMT is used to build a random forest. Our comparison results show that its performance is significantly better than the Random Forest (RF) algorithm.

Early effort estimation is both essential and challenging in software development projects. Accurate effort estimation mainly relies on accurate software sizing. COSMIC Function Point (CFP) is a means for measuring the functional size of software. It is a standardized method that can be applied to obtain size measurements based on early artifacts such as the software architecture design. In this paper, we present an industrial case study conducted to evaluate the correlation between CFP measurements performed at the architecture design phase and the actual development efforts. The study is performed in the context of a FinTech company that develops a system comprising a backend, a native iOS application, and a native Android application. We collect CFP calculations that are performed at the architecture design phase of the corresponding project. We also collect measurements regarding the actual effort for 12 months. Then, we investigate the correlation between CFP calculations and the measured effort. The actual effort can vary for some development tasks that are associated with the same CFP calculations. We analyze the relation of these variations with the level of developer experience and the type of target platform. Our results show that there is a significant correlation between size estimations made with CFP and the actual effort. The variance in effort per CFP among varying types of platforms is statistically significant. We also observe variance in effort per CFP among developers of varying expertise levels.

Abstract There have been success stories reported regarding the adoption of agile software development methods in the industry. There also exist observations on their limitations. One of these limitations is scalability since agile methods like Scrum were originally designed for small software teams. Scalable agile frameworks were introduced to address this limitation. We conducted an industrial case study on the adoption of such a framework, called Nexus. Our study involves quantitative and qualitative evaluation based on observations within a product development organization over a period of 12 months. Scrum is used for the development of a product during the first 6 months of this period. Nexus is used in the remaining 6 months. Data are collected throughout the whole period for measuring productivity, quality, and team member motivation. Results suggest a significant increase in productivity and product quality after switching to Nexus. Team motivation was slightly improved as well.