An ensemble of computational intelligence models for software maintenance effort prediction

  • Authors:

  • Hamoud Aljamaan;Mahmoud O. Elish;Irfan Ahmad

  • Affiliations:

  • Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia;Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia;Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

  • Venue:
  • IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
  • Year:
  • 2013

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Abstract

More accurate prediction of software maintenance effort contributes to better management and control of software maintenance. Several research studies have recently investigated the use of computational intelligence models for software maintainability prediction. The performance of these models however may vary from dataset to dataset. Consequently, computational intelligence ensemble techniques have become increasingly popular as they take advantage of the capabilities of their constituent models toward a dataset to come up with more accurate or at least competitive prediction accuracy compared to individual models. This paper proposes and empirically evaluates an ensemble of computational intelligence models for predicting software maintenance effort. The results confirm that the proposed ensemble technique provides more accurate prediction compared to individual models, and thus it is more reliable.