research-article

Testing data transformations in MapReduce programs

Authors:

Claudio de la Riva,

Javier TuyaAuthors Info & Claims

A-TEST 2015: Proceedings of the 6th International Workshop on Automating Test Case Design, Selection and Evaluation

Pages 20 - 25

https://doi.org/10.1145/2804322.2804326

Published: 30 August 2015 Publication History

Abstract

MapReduce is a parallel data processing paradigm oriented to process large volumes of information in data-intensive applications, such as Big Data environments. A characteristic of these applications is that they can have different data sources and data formats. For these reasons, the inputs could contain some poor quality data that could produce a failure if the program functionality does not handle properly the variety of input data. The output of these programs is obtained from a number of input transformations that represent the program logic. This paper proposes the testing technique called MRFlow that is based on data flow test criteria and oriented to transformations analysis between the input and the output in order to detect defects in MapReduce programs. MRFlow is applied over some MapReduce programs and detects several defects.

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Cited By

Mu XLiu LZhang PLi JLi H(2024)A method of test case set generation in the commutativity test of reduce functionsScience of Computer Programming10.1016/j.scico.2023.103006231:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.scico.2023.103006
Hasanpuri VDiwaker C(2023)Big Data, Bigger Challenges: A Comparative Study of Performance Testing2023 Seventh International Conference on Image Information Processing (ICIIP)10.1109/ICIIP61524.2023.10537762(870-875)Online publication date: 22-Nov-2023
https://doi.org/10.1109/ICIIP61524.2023.10537762
Hasanpuri VDiwaker C(2022)Comparative Analysis of Techniques for Big-data Performance Testing2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC56933.2022.10053306(292-297)Online publication date: 25-Nov-2022
https://doi.org/10.1109/PDGC56933.2022.10053306
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Index Terms

Testing data transformations in MapReduce programs

Recommendations

Testing MapReduce programs: A systematic mapping study
Summary Context
MapReduce is a processing model used in Big Data to facilitate the analysis of large data under a distributed architecture.
Objective
The aim of this study is to identify and categorize the state of the art of software testing in ...

State‐of‐the‐art of software testing in MapReduce applications through systematic mapping study: findings, gaps and opportunities. image image
Provable Improvements on Branch Testing

This paper compares the fault-detecting ability of several software test data adequacy criteria. It has previously been shown that if C/sub 1/ properly covers C/sub 2/, then C/sub 1/ is guaranteed to be better at detecting faults than C/sub 2/, in the ...
MapReduce: Review and open challenges

The continuous increase in computational capacity over the past years has produced an overwhelming flow of data or big data, which exceeds the capabilities of conventional processing tools. Big data signify a new era in data exploration and utilization. ...

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Published In

cover image ACM Conferences

A-TEST 2015: Proceedings of the 6th International Workshop on Automating Test Case Design, Selection and Evaluation

August 2015

46 pages

ISBN:9781450338134

DOI:10.1145/2804322

General Chair:
Tanja Vos,
Program Chairs:
Sigrid Eldh,
Wishnu Prasetya

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 30 August 2015

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ESEC/FSE'15

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ESEC/FSE'15: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

August 30 - 31, 2015

Bergamo, Italy

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ISSTA '25

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34th ACM SIGSOFT International Symposium on Software Testing and Analysis

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Cited By

Mu XLiu LZhang PLi JLi H(2024)A method of test case set generation in the commutativity test of reduce functionsScience of Computer Programming10.1016/j.scico.2023.103006231:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.scico.2023.103006
Hasanpuri VDiwaker C(2023)Big Data, Bigger Challenges: A Comparative Study of Performance Testing2023 Seventh International Conference on Image Information Processing (ICIIP)10.1109/ICIIP61524.2023.10537762(870-875)Online publication date: 22-Nov-2023
https://doi.org/10.1109/ICIIP61524.2023.10537762
Hasanpuri VDiwaker C(2022)Comparative Analysis of Techniques for Big-data Performance Testing2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC56933.2022.10053306(292-297)Online publication date: 25-Nov-2022
https://doi.org/10.1109/PDGC56933.2022.10053306
de Souza Neto JMartins Moreira AVargas‐Solar GMusicante M(2022)TRANSMUT‐Spark: Transformation mutation for Apache SparkSoftware Testing, Verification and Reliability10.1002/stvr.180932:8Online publication date: 10-Feb-2022
https://doi.org/10.1002/stvr.1809
Rai AMalviya A(2020)Testing MapReduce program using Induction Method2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS)10.1109/SCEECS48394.2020.178(1-5)Online publication date: Feb-2020
https://doi.org/10.1109/SCEECS48394.2020.178
de Souza Neto JMartins Moreira AVargas-Solar GMusicante M(2020)Mutation Operators for Large Scale Data Processing Programs in SparkAdvanced Information Systems Engineering10.1007/978-3-030-49435-3_30(482-497)Online publication date: 3-Jun-2020
https://doi.org/10.1007/978-3-030-49435-3_30
Morán Jde la Riva CTuya J(2019)Testing MapReduce programsJournal of Software: Evolution and Process10.1002/smr.212031:3Online publication date: 25-Mar-2019
https://dl.acm.org/doi/10.1002/smr.2120
Moran JBertolino Ade la Riva CTuya J(2018)Automatic Testing of Design Faults in MapReduce ApplicationsIEEE Transactions on Reliability10.1109/TR.2018.280204767:3(717-732)Online publication date: Sep-2018
https://doi.org/10.1109/TR.2018.2802047
Feng HChandrasekaran JLei YKacker RKuhn D(2018)A Method-Level Test Generation Framework for Debugging Big Data Applications2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622248(221-230)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622248
Moran JBertolino Ade la Riva CTuya J(2017)Towards Ex Vivo Testing of MapReduce Applications2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS.2017.17(73-80)Online publication date: Jul-2017
https://doi.org/10.1109/QRS.2017.17
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