Javed O; Singh P; Reger G; Toor S
To test, or not to test: A proactive approach for deciding complete performance test initiation Conference
2022 IEEE International Conference on Big Data (Big Data), IEEE, 2022.
Abstract | Links | BibTeX | Tags: Classification, Software, Unsupervised Learning
@conference{nokey,
title = {To test, or not to test: A proactive approach for deciding complete performance test initiation},
author = {Omar Javed and Prashant Singh and Gilles Reger and Salman Toor},
url = {https://doi.ieeecomputersociety.org/10.1109/BigData55660.2022.10020543},
doi = {10.1109/BigData55660.2022.10020543},
year = {2022},
date = {2022-07-01},
urldate = {2022-07-01},
booktitle = {2022 IEEE International Conference on Big Data (Big Data)},
pages = {4758-4767},
publisher = {IEEE},
abstract = {Software performance testing requires a set of inputs that exercise different sections of the code to identify performance issues. However, running tests on a large set of inputs can be a very time consuming process. It is even more problematic when test inputs are constantly growing, which is the case with a large-scale scientific organization such as CERN where the process of performing scientific experiment generates plethora of data that is analyzed by physicists leading to new scientific discoveries. Therefore, in this article, we present a test input minimization approach based on a clustering technique to handle the issue of testing on growing data. Furthermore, we use clustering information to propose an automatic approach that recommends the tester to decide when to run the complete test suite for performance testing. To demonstrate the efficacy of our approach, we applied it to two different code updates of a web service which is used at CERN and we found that the recommendation for performance test initiation made by our approach for an update with bottleneck is valid.},
keywords = {Classification, Software, Unsupervised Learning},
pubstate = {published},
tppubtype = {conference}
}
Software performance testing requires a set of inputs that exercise different sections of the code to identify performance issues. However, running tests on a large set of inputs can be a very time consuming process. It is even more problematic when test inputs are constantly growing, which is the case with a large-scale scientific organization such as CERN where the process of performing scientific experiment generates plethora of data that is analyzed by physicists leading to new scientific discoveries. Therefore, in this article, we present a test input minimization approach based on a clustering technique to handle the issue of testing on growing data. Furthermore, we use clustering information to propose an automatic approach that recommends the tester to decide when to run the complete test suite for performance testing. To demonstrate the efficacy of our approach, we applied it to two different code updates of a web service which is used at CERN and we found that the recommendation for performance test initiation made by our approach for an update with bottleneck is valid. Javed O; Singh P; Reger G; Toor S
To test, or not to test: A proactive approach for deciding complete performance test initiation Journal Article
In: arXiv preprint arXiv:2205.14749, 2022.
Links | BibTeX | Tags: Software, Unsupervised Learning
@article{javed2022test,
title = {To test, or not to test: A proactive approach for deciding complete performance test initiation},
author = {Omar Javed and Prashant Singh and Giles Reger and Salman Toor},
url = {https://arxiv.org/pdf/2205.14749},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2205.14749},
keywords = {Software, Unsupervised Learning},
pubstate = {published},
tppubtype = {article}
}
To test, or not to test: A proactive approach for deciding complete performance test initiation Conference
2022 IEEE International Conference on Big Data (Big Data), IEEE, 2022.
To test, or not to test: A proactive approach for deciding complete performance test initiation Journal Article
In: arXiv preprint arXiv:2205.14749, 2022.