Circumstantial-Evidence-Based Judgment for Software Effort Estimation

TL;DR

This paper introduces a circumstantial-evidence-based approach for software effort estimation that leverages development experiences and rational inference to qualitatively estimate effort, complementing traditional expert judgment.

Contribution

It proposes a novel circumstantial-evidence-based judgment method using propositional learning theory for effort estimation, addressing data limitations in expert judgment.

Findings

Demonstrated effectiveness in effort tradeoff analysis for Web service projects

Shows how circumstantial evidence can complement expert judgment

Facilitates early effort estimation before project implementation

Abstract

Expert judgment for software effort estimation is oriented toward direct evidences that refer to actual effort of similar projects or activities through experts' experiences. However, the availability of direct evidences implies the requirement of suitable experts together with past data. The circumstantial-evidence-based judgment proposed in this paper focuses on the development experiences deposited in human knowledge, and can then be used to qualitatively estimate implementation effort of different proposals of a new project by rational inference. To demonstrate the process of circumstantial-evidence-based judgment, this paper adopts propositional learning theory based diagnostic reasoning to infer and compare different effort estimates when implementing a Web service composition project with some different techniques and contexts. The exemplar shows our proposed work can help determine effort tradeoff before project implementation. Overall, circumstantial-evidence-based judgment is not an alternative but complementary to expert judgment so as to facilitate and improve software effort estimation.