An evolutionary ensemble analogy‐based software effort estimation. (26th October 2021)

• Record Type: Journal Article
• Authors: Shahpar, Zahra; Bardsiri, Vahid Khatibi; Bardsiri, Amid Khatibi
• Other Names: Gill Sukhpal Singh guestEditor.; Vinuesa Ricardo guestEditor.; Balasubramanian Venki guestEditor.; Ghosh Soumya K. guestEditor.
• Is Part Of: Software, practice & experience. Volume 52:Number 4(2022)
• Page Start: 929
• Page End: 946
• Publication Date: 2021-10-26
• Subjects: analogy‐based estimation -- ensemble effort estimation -- genetic algorithm -- software effort estimation; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3040
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Analogy‐based estimation (ABE) is the most commonly applied method for estimating using software. Although there are several analogy‐based estimation techniques, there is no consistent conclusion of which technique is the best in all circumstances. Therefore, this article presents an evolutionary ensemble ABE (EEABE) for software cost estimation. Ensemble effort estimation (EEE) models forecast software development endeavors through using multiple estimation methods. In this article, EEABE combines GA as an evolutionary algorithm with six ABE models. The proposed method has been evaluated on four well‐known datasets comprising Maxwell, Albrecht, Kemerer, and Desharnais, by the k‐fold cross‐validation technique and based on the performance criteria of MRE, MMRE, MDMRE, BMMRE, and PRED(0.25). The simulation results demonstrate that the use of EEABE increases the accuracy of estimation and reduces the cost. Besides, EEABE is a flexible and adaptable model with any type of dataset and software development project, and it has been optimized with the possibility of using various ABE techniques.