Utilizing Faults and Time to Finish Estimating the Number of Software Test Workers Using Artificial Neural Networks and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{sheta:2019:ISTSL,

• author = "Alaa Sheta and Sultan Aljahdali and Malik Braik",
• title = "Utilizing Faults and Time to Finish Estimating the Number of Software Test Workers Using Artificial Neural Networks and Genetic Programming",
• booktitle = "International Conference Europe Middle East \& North Africa Information Systems and Technologies to Support Learning, EMENA-ISTL 2018",
• year = "2019",
• editor = "Alvaro Rocha and Mohammed Serrhini",
• volume = "111",
• series = "Smart Innovation, Systems and Technologies",
• pages = "613--624",
• address = "Fez, Morocco",
• month = "25-27 " # oct,
• organisation = "AISTI",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, SBSE, ANN, Prediction of test workers, Software testing, Project management, Artificial Neural Networks",
• isbn13 = "978-3-030-03576-1",
• ISSN = "2190-3018",
• URL = "http://link.springer.com/chapter/10.1007/978-3-030-03577-8_67",
• DOI = "doi:10.1007/978-3-030-03577-8_67",
• abstract = "Time, effort and the estimation of number of staff desired are critical tasks for project managers and particularly for software projects. The software testing process signifies about 40-50percent of the software development lifecycle. Faults are detected and corrected during software testing. Accurate prediction of the number of test workers necessary to test a software before the delivery to a customer will save time and effort. In this paper, we present two models for estimating the number of test workers required for software testing using Artificial Neural Networks (ANN) and Genetic Programming (GP). We use the expected time to finish testing and the rate of change of fault observation as inputs to the proposed models. The proposed models were able to predict the required team size; thus, supporting project managers in allocating the team effort to various project phases. Both models yielded promising estimation results in real-time applications.",

}

Genetic Programming entries for Alaa Sheta Sultan Aljahdali Malik Shehadeh Braik

Citations