Optimized Fuzzy Logic Based Framework for Effort Estimation in Software Development

TL;DR

This paper introduces an optimized fuzzy logic framework for early-stage software effort estimation, enhancing accuracy and reliability by addressing input imprecision and incorporating expert knowledge.

Contribution

It extends the traditional COCOMO model with fuzzy logic to better handle vagueness and adapt to changing data environments.

Findings

Improved effort estimation accuracy over traditional models

Framework tolerates input imprecision and incorporates expert knowledge

Provides transparent and adaptable prediction mechanisms

Abstract

Software effort estimation at early stages of project development holds great significance for the industry to meet the competitive demands of today's world. Accuracy, reliability and precision in the estimates of effort are quite desirable. The inherent imprecision present in the inputs of the algorithmic models like Constructive Cost Model (COCOMO) yields imprecision in the output, resulting in erroneous effort estimation. Fuzzy logic based cost estimation models are inherently suitable to address the vagueness and imprecision in the inputs, to make reliable and accurate estimates of effort. In this paper, we present an optimized fuzzy logic based framework for software development effort prediction. The said framework tolerates imprecision, incorporates experts knowledge, explains prediction rationale through rules, offers transparency in the prediction system, and could adapt to changing environments with the availability of new data. The traditional cost estimation model COCOMO is extended in the proposed study by incorporating the concept of fuzziness into the measurements of size, mode of development for projects and the cost drivers contributing to the overall development effort.