Authors:
Mohamed Hosni
1
;
Ibtissam Medarhri
2
and
Juan Manuel Carrillo de Gea
3
Affiliations:
1
MOSI Research Team, LM2S3, ENSAM, Moulay Ismail University of Meknes, Morocco
;
2
MMCS Research Team, LMAID, ENSMR-Rabat, Morocco
;
3
Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain
Keyword(s):
Software Testing, Software Testing Effort, Machine Learning, Ensemble Method, ISBSG.
Abstract:
Delivering an accurate estimation of the effort required for software system development is crucial for the success of any software project. However, the software development lifecycle (SDLC) involves multiple activities, such as software design, software build, and software testing, among others. Software testing (ST) holds significant importance in the SDLC as it directly impacts software quality. Typically, the effort required for the testing phase is estimated as a percentage of the overall predicted SDLC effort, typically ranging between 10% and 60%. However, this approach poses risks as it hinders proper resource allocation by managers. Despite the importance of this issue, there is limited research available on estimating ST effort. This paper aims to address this concern by proposing four machine learning (ML) techniques and a heterogeneous ensemble to predict the effort required for ST activities. The ML techniques employed include K-nearest neighbor (KNN), Support Vector Re
gression, Multilayer Perceptron Neural Networks, and decision trees. The dataset used in this study was obtained from a well-known repository. Various unbiased performance indicators were utilized to evaluate the predictive capabilities of the proposed techniques. The overall results indicate that the KNN technique outperforms the other ML techniques, and the proposed ensemble showed superior performance accuracy compared to the remaining ML techniques.
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