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  5. DYNAMOJM: A JMeter Tool for Performance Testing Using Dynamic Workload Adaptation
 
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DYNAMOJM: A JMeter Tool for Performance Testing Using Dynamic Workload Adaptation

Author(s)
Huerta-Guevara, Osvaldo  
Ayala-Rivera, Vanessa  
Murphy, Liam, B.E.  
Portillo Dominguez, Andres Omar  
Uri
http://hdl.handle.net/10197/11288
Date Issued
2019-10-08
Date Available
2020-02-26T14:28:20Z
Embargo end date
2020-10-08
Abstract
Performance testing is a critical task to assure optimal experience for users, especially when there are high loads of concurrent users. JMeter is one of the most widely used tools for load and stress testing. With JMeter, it is possible to test the performance of static and dynamic resources on the web. This paper presents DYNAMOJM, a novel tool built on top of JMeter that enables testers to create a dynamic workload for performance testing. This tool implements the DYNAMO approach, which has proven useful to find performance issues more efficiently than static testing techniques.
Sponsorship
European Commission - European Regional Development Fund
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Springer
Series
Lecture Notes in Computer Science (LCNS, volume 11812)
Copyright (Published Version)
2019 International Federation for Information Processing
Subjects

Software engineering

Performance testing

Performance bug

Workload

Web systems and appli...

DOI
10.1007/978-3-030-31280-0_14
Language
English
Status of Item
Peer reviewed
Conference Details
The 31st International Federation for Information Processing (IFIP) International Conference on Testing Software and Systems (ICTSS), Paris, France, 15-17 October 2019
ISBN
9783030312794
ISSN
0302-9743
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
No Thumbnail Available
Name

ohuertag_ICTSS_2019_2.pdf

Size

1002.46 KB

Format

Adobe PDF

Checksum (MD5)

0a290c93ffdb52107aad24d33814d3dc

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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