A systematic literature review of software effort prediction using machine learning methods. Issue 10 (25th July 2019)

• Record Type: Journal Article
• Authors: Ali, Asad; Gravino, Carmine
• Is Part Of: Journal of software. Volume 31:Issue 10(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-25
• Subjects: accuracy measure -- effort estimation -- machine learning -- SLR -- software engineering; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2211
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Machine learning (ML) techniques have been widely investigated for building prediction models, able to estimate software development effort as well as to improve the accuracy of other estimation techniques. The objective of this paper is to systematically review the recent studies which used and discussed the software effort estimation models built using ML techniques. The performed literature review is based on the empirical studies published in the time period of January 1991 to December 2017, by employing widely used guidelines. The review has selected a total of 75 primary studies after the careful filtering of inclusion/exclusion and quality assessment criteria. The performed analysis reveals that artificial neural network (ANN) as ML model, NASA as dataset, and mean magnitude of relative error (MMRE) as accuracy measure are widely used in the selected studies. ANN and support vector machine (SVM) are the two techniques which have outperformed other ML techniques in more studies. Regression techniques are the mostly used among the non‐ML techniques, which outperformed other ML techniques in about 19 studies. Moreover, SVM and regression techniques in combination are characterized by better predictions when compared with other ML and non‐ML techniques.