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. Singh P; van der Herten J; Deschrijver D; Couckuyt I; Dhaene T
A sequential sampling strategy for adaptive classification of computationally expensive data Journal Article
In: Structural and Multidisciplinary Optimization, vol. 55, no. 4, pp. 1425–1438, 2017.
Links | BibTeX | Tags: Classification, Inverse Problem, Optimization, Sampling, Surrogate Modeling
@article{singh2017sequential,
title = {A sequential sampling strategy for adaptive classification of computationally expensive data},
author = {Prashant Singh and Joachim van der Herten and Dirk Deschrijver and Ivo Couckuyt and Tom Dhaene},
url = {https://link.springer.com/article/10.1007/s00158-016-1584-1
https://users.ugent.be/~didschri/papers/2017_04_Springer_SMO.pdf
},
doi = {https://doi.org/10.1007/s00158-016-1584-1},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Structural and Multidisciplinary Optimization},
volume = {55},
number = {4},
pages = {1425--1438},
publisher = {Springer Berlin Heidelberg},
keywords = {Classification, Inverse Problem, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Ferranti F; Deschrijver D; Couckuyt I; Dhaene T
Classification aided domain reduction for high dimensional optimization Proceedings Article
In: Proceedings of the Winter Simulation Conference 2014, pp. 3928–3939, IEEE 2014.
Links | BibTeX | Tags: Classification, Optimization, Sampling, Surrogate Modeling
@inproceedings{singh2014classification,
title = {Classification aided domain reduction for high dimensional optimization},
author = {Prashant Singh and Francesco Ferranti and Dirk Deschrijver and Ivo Couckuyt and Tom Dhaene},
url = {https://ieeexplore.ieee.org/abstract/document/7020218
https://biblio.ugent.be/publication/5955500/file/5955522},
doi = {https://doi.org/10.1109/WSC.2014.7020218},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Proceedings of the Winter Simulation Conference 2014},
pages = {3928--3939},
organization = {IEEE},
keywords = {Classification, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Deschrijver D; Pissoort D; Dhaene T
Adaptive classification algorithm for EMC-compliance testing of electronic devices Journal Article
In: Electronics Letters, vol. 49, no. 24, pp. 1526–1528, 2013.
Links | BibTeX | Tags: Classification, Inverse Problem, Optimization, Surrogate Modeling
@article{singh2013adaptive,
title = {Adaptive classification algorithm for EMC-compliance testing of electronic devices},
author = {Prashant Singh and Dirk Deschrijver and Davy Pissoort and Tom Dhaene},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/el.2013.2766
https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/el.2013.2766},
doi = {https://doi.org/10.1049/el.2013.2766},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {Electronics Letters},
volume = {49},
number = {24},
pages = {1526--1528},
publisher = {The Institution of Engineering and Technology},
keywords = {Classification, Inverse Problem, Optimization, Surrogate Modeling},
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.
A sequential sampling strategy for adaptive classification of computationally expensive data Journal Article
In: Structural and Multidisciplinary Optimization, vol. 55, no. 4, pp. 1425–1438, 2017.
Classification aided domain reduction for high dimensional optimization Proceedings Article
In: Proceedings of the Winter Simulation Conference 2014, pp. 3928–3939, IEEE 2014.
Adaptive classification algorithm for EMC-compliance testing of electronic devices Journal Article
In: Electronics Letters, vol. 49, no. 24, pp. 1526–1528, 2013.