Empirical Analysis on Productivity Prediction and Locality for Use Case Points Method

TL;DR

This study investigates how local data partitioning based on environmental factors improves effort prediction accuracy in the Use Case Points method using machine learning models.

Contribution

It introduces local data partitioning techniques and demonstrates their effectiveness in enhancing productivity and effort prediction models over traditional global approaches.

Findings

Local data models outperform global models in effort prediction.

Partitioning by environmental factors improves model accuracy.

Locality does not require strict assumptions about productivity-environment relationships.

Abstract

Use Case Points (UCP) method has been around for over two decades. Although, there was a substantial criticism concerning the algebraic construction and factors assessment of UCP, it remains an efficient early size estimation method. Predicting software effort from UCP is still an ever-present challenge. The earlier version of UCP method suggested using productivity as a cost driver, where fixed or a few pre-defined productivity ratios have been widely agreed. While this approach was successful when no enough historical data is available, it is no longer acceptable because software projects are different in terms of development aspects. Therefore, it is better to understand the relationship between productivity and other UCP variables. This paper examines the impact of data locality approaches on productivity and effort prediction from multiple UCP variables. The environmental factors are used as partitioning factors to produce local homogeneous data either based on their influential levels or using clustering algorithms. Different machine learning methods, including solo and ensemble methods, are used to construct productivity and effort prediction models based on the local data. The results demonstrate that the prediction models that are created based on local data surpass models that use entire data. Also, the results show that conforming the hypothetical assumption between productivity and environmental factors is not necessarily a requirement for success of locality.