Optimizing Parameters of Software Effort Estimation Models using Directed Artificial Bee Colony Algorithm

Abstract

Effective software effort estimation is one of the challenging tasks in software engineering. There has been various alternatives introduced to enhance the accuracy in predictions. In this respect, estimation approaches based on algorithmic models have been widely used. These models consider modeling  software effort as a function of the size of the developed project. However, most approaches sharing a common thread of complex mathematical models face the difficulties in parameters calibration and tuning. This study proposes using a directed artificial bee colony algorithm in order to tune the values of model parameters based on past actual effort. The proposed methods were verified with NASA software dataset and the obtained results were compared to the existing models in other literatures. The results indicated that our proposal has significantly improved the performance of the estimations.Effective software effort estimation is one of the challenging tasks in software engineering. There has been various alternatives introduced to enhance the accuracy in predictions. In this respect, estimation approaches based on algorithmic models have been widely used. These models consider modeling  software effort as a function of the size of the developed project. However, most approaches sharing a common thread of complex mathematical models face the difficulties in parameters calibration and tuning. This study proposes using a directed artificial bee colony algorithm in order to tune the values of model parameters based on past actual effort. The proposed methods were verified with NASA software dataset and the obtained results were compared to the existing models in other literatures. The results indicated that our proposal has significantly improved the performance of the estimations.

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Authors

  • Thanh Tung Khuat The University of Danang, University of Science and Technology
  • My Hanh Le The University of Danang, University of Science and Technology

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Published

10/24/2016

How to Cite

Khuat, T. T., & Le, M. H. (2016). Optimizing Parameters of Software Effort Estimation Models using Directed Artificial Bee Colony Algorithm. Informatica, 40(4). Retrieved from https://www.informatica.si/index.php/informatica/article/view/1043

Issue

Vol. 40 No. 4 (2016)

Section

Regular papers

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