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. Ekmefjord M; Ait-Mlouk A; Alawadi S; Åkesson M; Singh P; Spjuth O; Toor S; Hellander A
Scalable federated machine learning with fedn Proceedings Article
In: 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 555–564, IEEE 2022.
Links | BibTeX | Tags: Federated Learning, Software
@inproceedings{ekmefjord2022scalable,
title = {Scalable federated machine learning with fedn},
author = {Morgan Ekmefjord and Addi Ait-Mlouk and Sadi Alawadi and Mattias Åkesson and Prashant Singh and Ola Spjuth and Salman Toor and Andreas Hellander},
url = {https://ieeexplore.ieee.org/abstract/document/9826069
https://arxiv.org/pdf/2103.00148
},
doi = {https://doi.org/10.1109/CCGrid54584.2022.00065},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)},
pages = {555--564},
organization = {IEEE},
keywords = {Federated Learning, Software},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}
Jiang R; Jacob B; Geiger M; Matthew S; Rumsey B; Singh P; Wrede F; Yi T; Drawert B; Hellander A; others
Epidemiological modeling in stochss live! Journal Article
In: Bioinformatics, vol. 37, no. 17, pp. 2787–2788, 2021.
Links | BibTeX | Tags: Bayesian Inference, Software
@article{jiang2021epidemiological,
title = {Epidemiological modeling in stochss live!},
author = {Richard Jiang and Bruno Jacob and Matthew Geiger and Sean Matthew and Bryan Rumsey and Prashant Singh and Fredrik Wrede and Tau-Mu Yi and Brian Drawert and Andreas Hellander and others},
url = {https://academic.oup.com/bioinformatics/article/37/17/2787/6123781
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab061/40342592/btab061.pdf},
doi = {https://doi.org/10.1093/bioinformatics/btab061},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {17},
pages = {2787--2788},
publisher = {Oxford University Press},
keywords = {Bayesian Inference, Software},
pubstate = {published},
tppubtype = {article}
}
Singh P; Wrede F; Hellander A
Scalable machine learning-assisted model exploration and inference using Sciope Journal Article
In: Bioinformatics, vol. 37, no. 2, pp. 279–281, 2021.
Links | BibTeX | Tags: Bayesian Inference, Deep Learning, Inverse Problem, Optimization, Software, Surrogate Modeling
@article{singh2021scalable,
title = {Scalable machine learning-assisted model exploration and inference using Sciope},
author = {Prashant Singh and Fredrik Wrede and Andreas Hellander},
url = {https://academic.oup.com/bioinformatics/article/37/2/279/5876021},
doi = {https://doi.org/10.1093/bioinformatics/btaa673},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {2},
pages = {279--281},
publisher = {Oxford University Press},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Optimization, Software, 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.
Scalable federated machine learning with fedn Proceedings Article
In: 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 555–564, 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.
Epidemiological modeling in stochss live! Journal Article
In: Bioinformatics, vol. 37, no. 17, pp. 2787–2788, 2021.
Scalable machine learning-assisted model exploration and inference using Sciope Journal Article
In: Bioinformatics, vol. 37, no. 2, pp. 279–281, 2021.