For software project planning control and management, an accurate estimate of software development cost is important. Past research has focused on using parametric models to predict development cost based on attributes such as lines of code or function points. This requires researchers to identify the set of factors that influence cost estimation before the system is constructed. We propose a non-parametric approach that integrates a neural network method with cluster analysis to estimate development cost. The integration of the two techniques not only allows for a more accurate cost estimate but also leads to an increase in the training efficacy of the network.
|Number of pages||9|
|Journal||Information and Management|
|State||Published - Aug 5 1998|
Bibliographical noteFunding Information:
Dr. Balakrishnan's research is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
- Cluster analysis
- Machine learning
- Neural network
- Software development cost
ASJC Scopus subject areas
- Management Information Systems
- Information Systems
- Information Systems and Management