Software stage-effort estimation based on association rule mining and fuzzy set theory

TL;DR

This paper presents a novel approach combining association rule mining and fuzzy set theory to improve stage-effort estimation in software projects, addressing early prediction uncertainties and aiding better resource management.

Contribution

It introduces a new model that leverages prior effort data with fuzzy logic and association rules for more accurate stage-effort prediction in software development.

Findings

High prediction accuracy demonstrated

Potential for improved resource allocation

Effective handling of uncertainty in effort estimation

Abstract

Relaying on early effort estimation to predict the required number of resources is not often sufficient, and could lead to under or over estimation. It is widely acknowledge that that software development process should be refined regularly and that software prediction made at early stage of software development is yet kind of guesses. Even good predictions are not sufficient with inherent uncertainty and risks. The stage-effort estimation allows project manager to re-allocate correct number of resources, re-schedule project and control project progress to finish on time and within budget. In this paper we propose an approach to utilize prior effort records to predict stage effort. The proposed model combines concepts of Fuzzy set theory and association rule mining. The results were good in terms of prediction accuracy and have potential to deliver good stage-effort estimation.