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access icon free Comparative analysis of soft computing techniques for predicting software effort based use case points

  • Author(s): Mohammad Azzeh 1 ; Ali Bou Nassif 2 ; Shadi Banitaan 3
    • View affiliations
  • Source: Volume 12, Issue 1, February 2018, p. 19 – 29
    DOI:  10.1049/iet-sen.2016.0322 , Print ISSN 1751-8806, Online ISSN 1751-8814
© The Institution of Engineering and Technology
Received 17/12/2016, Accepted 05/09/2017, Revised 23/08/2017, Published 14/09/2017

The size of a software project is a key measure of predicting software effort at the requirements and analysis phase. Use case points (UCP) is among software size metrics that achieved good reputation because of the increasing popularity of use case driven development methodologies in software industry. Nevertheless, there is no consistent method that can effectively translate the UCP into its corresponding effort. Previous estimation models were built using a very limited number of projects, and they were not well examined. The soft computing techniques were rarely applied for such problem and their performances have not been well investigated using a systematic procedure. This study looks into the accuracy and stability of some soft computing methods for the problem of effort estimation based on UCP. Four neural network methods, adaptive neuro fuzzy inference system and support vector regression have been used in this comparative study. The results suggest that most used soft computing techniques can work well with good accuracy for such problem. Among them, the general regression neural network is the superior one with stable ranking across different accuracy measures. Also, it has been found that using adjustment variables with basic UCP variables, solely or together, have positive impact on the accuracy and stability.

Inspec keywords: software management; project management; regression analysis; neural nets; software metrics; support vector machines; systems analysis; fuzzy reasoning

Other keywords: software size metrics; adaptive neuro fuzzy inference system; requirements phase; software effort prediction; regression neural network; software project; UCP; support vector regression; use case points; use case driven development; analysis phase; software industry; soft computing

Subjects: Neural computing techniques; Knowledge engineering techniques; Other topics in statistics; Software metrics; Software management

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