Improving Case Based Software Effort Estimation Using a Multi-Criteria Decision Technique

Date

2019

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Springer International Publishing AG

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Abstract

Producing an accurate effort estimate is essential for effective software project management, and yet remains highly challenging and difficult to achieve, especially at the early stage of software development, because very little detail about the project are known at its beginning. To cope with this challenge, we present a novel framework for software effort estimation, which takes an incremental approach on one hand, using a case-based reasoning (CBR) model, while considering a comprehensive set of different types of requirements models on the other hand, including functional requirements (FRs), non-functional requirements (NFRs), and domain properties (DPs). Concerning the use of CBR, this framework offers a multi-criteria technique for enhancing the accuracy of similarity measures among cases of multiple past projects that are similar to the current software project, towards determining and selecting the most similar one. We have tested our proposed framework on 36 (students') projects and the results are very encouraging, in the sense that the difference between the estimated effort and the actual effort was lower than 10% in most cases.

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Keywords

Case-based reasoning (cbr), Decision making, Costs, Industrial--Estimates--Data processing, Neural networks (Computer science), Analogy, Regression analysis, Computer science

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©2019 Springer International Publishing AG, part of Springer Nature

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