Predicting software development project outcomes

Authors:
Rosina Weber;Michael Waller;June Verner;William Evanco
Affiliations:
College of Information Science & Technology, Drexel University, Philadelphia, PA;College of Information Science & Technology, Drexel University, Philadelphia, PA;College of Information Science & Technology, Drexel University, Philadelphia, PA;College of Information Science & Technology, Drexel University, Philadelphia, PA
Venue:
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Year:
2003

Citing 7
Cited 3

Case-based reasoning: foundational issues, methodological variations, and system approaches

AI Communications
A comparison of software effort estimation techniques: using function points with neural networks, case-based reasoning and regression models

Journal of Systems and Software
Critical Success Factors In Software Projects

IEEE Software
Explanation-Based Generalization: A Unifying View

Machine Learning
Weighting Features

ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Knowledge Management and Case-Based Reasoning: A Perfect Match?

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Using CBR to Estimate Development Effort for Web Hypermedia Applications

Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference

Evaluating the Perceived Effect of Software Engineering Practices in the Italian Industry

ICSP '09 Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes
Evaluating logistic regression models to estimate software project outcomes

Information and Software Technology
The optimization of success probability for software projects using genetic algorithms

Journal of Systems and Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

Case-based reasoning is a flexible methodology to manage software development related tasks. However, when the reasoner's task is prediction, there are a number of different CBR techniques that could be chosen to address the characteristics of a dataset. We examine several of these techniques to assess their accuracy in predicting software development project outcomes (i.e., whether the project is a success or failure) and identify critical success factors within our data. We collected the data from software developers who answered a questionnaire targeting a software development project they had recently worked on. The questionnaire addresses both technical and managerial features of software development projects. The results of these evaluations are compared with results from logistic regression analysis, which serves as a comparative baseline. The research in this paper can guide design decisions in future CBR implementations to predict the outcome of projects described with managerial factors.