Authors:
Martin Auer
;
Bernhard Graser
and
Stefan Biffl
Affiliation:
Institute for Software Technology, Vienna University of Technology, Austria
Keyword(s):
Software project portfolio, portfolio decisions, portfolio visualization, multidimensional scaling, analogy-based cost estimation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Case-Based Reasoning
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
;
Web Information Systems and Technologies
Abstract:
Software cost estimation is a crucial task in software project portfolio decisions like start scheduling, resource allocation, or bidding. A variety of estimation methods have been proposed to support estimators.
Especially the analogy-based approach—based on a project’s similarities with past projects—has been reported as both efficient and relatively transparent. However, its performance was typically measured automatically and the effect of human estimators’ sanity checks was neglected.
Thus, this paper proposes the visualization of high-dimensional software project portfolio data using multidimensional scaling (MDS). We (i) propose data preparation steps for an MDS visualization of software portfolio data, (ii) visualize several real-world industry project portfolio data sets and quantify the achieved approximation quality to assess the feasibility, and (iii) outline the expected benefits referring to the visualized portfolios’ properties.
This approach offers several promisin
g benefits by enhancing portfolio data understanding and by providing intuitive means for estimators to assess an estimate’s plausibility.
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