Circumstantial-Evidence-Based Judgment for Software Effort Estimation
This work addresses software project planning for managers and developers by providing a complementary method to expert judgment, though it appears incremental as it builds on existing diagnostic reasoning approaches.
The paper tackles the problem of software effort estimation by proposing circumstantial-evidence-based judgment, which uses human knowledge and rational inference to qualitatively estimate implementation effort for new projects, demonstrating it can help determine effort tradeoffs before implementation.
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.