Software development cost estimation: Integrating neural network with cluster analysis

Anita Lee, Chun Hung Cheng, Jaydeep Balakrishnan

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


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.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalInformation and Management
Issue number1
StatePublished - Aug 5 1998

Bibliographical note

Funding 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


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