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Mutation Operators for Large Scale Data Processing Programs in Spark
This paper proposes a mutation testing approach for big data processing programs that follow a data flow model, such as those implemented on top of Apache Spark. Mutation testing is a fault-based technique that relies on fault simulation by modifying programs, to create faulty versions called mutant...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266460/ http://dx.doi.org/10.1007/978-3-030-49435-3_30 |
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author | de Souza Neto, João Batista Martins Moreira, Anamaria Vargas-Solar, Genoveva Musicante, Martin Alejandro |
author_facet | de Souza Neto, João Batista Martins Moreira, Anamaria Vargas-Solar, Genoveva Musicante, Martin Alejandro |
author_sort | de Souza Neto, João Batista |
collection | PubMed |
description | This paper proposes a mutation testing approach for big data processing programs that follow a data flow model, such as those implemented on top of Apache Spark. Mutation testing is a fault-based technique that relies on fault simulation by modifying programs, to create faulty versions called mutants. Mutant creation is carried on by operators able to simulate specific and well identified faults. A testing process must be able to signal faults within mutants and thereby avoid having ill behaviours within a program. We propose a set of mutation operators designed for Spark programs characterized by a data flow and data processing operations. These operators model changes in the data flow and operations, to simulate faults that take into account Spark program characteristics. We performed manual experiments to evaluate the proposed mutation operators in terms of cost and effectiveness. Thereby, we show that mutation operators can contribute to the testing process, in the construction of reliable Spark programs. |
format | Online Article Text |
id | pubmed-7266460 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72664602020-06-03 Mutation Operators for Large Scale Data Processing Programs in Spark de Souza Neto, João Batista Martins Moreira, Anamaria Vargas-Solar, Genoveva Musicante, Martin Alejandro Advanced Information Systems Engineering Article This paper proposes a mutation testing approach for big data processing programs that follow a data flow model, such as those implemented on top of Apache Spark. Mutation testing is a fault-based technique that relies on fault simulation by modifying programs, to create faulty versions called mutants. Mutant creation is carried on by operators able to simulate specific and well identified faults. A testing process must be able to signal faults within mutants and thereby avoid having ill behaviours within a program. We propose a set of mutation operators designed for Spark programs characterized by a data flow and data processing operations. These operators model changes in the data flow and operations, to simulate faults that take into account Spark program characteristics. We performed manual experiments to evaluate the proposed mutation operators in terms of cost and effectiveness. Thereby, we show that mutation operators can contribute to the testing process, in the construction of reliable Spark programs. 2020-05-30 /pmc/articles/PMC7266460/ http://dx.doi.org/10.1007/978-3-030-49435-3_30 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article de Souza Neto, João Batista Martins Moreira, Anamaria Vargas-Solar, Genoveva Musicante, Martin Alejandro Mutation Operators for Large Scale Data Processing Programs in Spark |
title | Mutation Operators for Large Scale Data Processing Programs in Spark |
title_full | Mutation Operators for Large Scale Data Processing Programs in Spark |
title_fullStr | Mutation Operators for Large Scale Data Processing Programs in Spark |
title_full_unstemmed | Mutation Operators for Large Scale Data Processing Programs in Spark |
title_short | Mutation Operators for Large Scale Data Processing Programs in Spark |
title_sort | mutation operators for large scale data processing programs in spark |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266460/ http://dx.doi.org/10.1007/978-3-030-49435-3_30 |
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