International Journal of Networked and Distributed Computing

Volume 8, Issue 1, December 2019, Pages 41 - 48

Machine Learning in Failure Regions Detection and Parameters Analysis

Authors
Saeed Abdel Wahab*, Reem El Adawi, Ahmed Khater
Department of Calibre D2S, Mentor, a Siemens Business, Cairo, Egypt
*Corresponding author. Email: Saeed_AbdelWahab@mentor.com
Corresponding Author
Saeed Abdel Wahab
Received 23 April 2019, Accepted 20 May 2019, Available Online 24 December 2019.
DOI
https://doi.org/10.2991/ijndc.k.191204.001How to use a DOI?
Keywords
Testing, automation, machine learning, clustering, classification
Abstract

Testing automation is one of the challenges facing the software development industry, especially for large complex products. This paper proposes a mechanism called Multi Stage Failure Detector (MSFD) for automating black box testing using different machine learning algorithms. The input to MSFD is the tool’s set of parameters and their value ranges. The parameter values are randomly sampled to produce a large number of parameter combinations that are fed into the software under test. Using neural networks, the resulting logs from the tool are classified into passing and failing logs and the failing logs are then clustered (using mean-shift clustering) into different failure types. MSFD provides visualization of the failures along with the responsible parameters. Experiments on and results for two real-world complex software products are provided, showing the ability of MSFD to detect all failures and cluster them into the correct failure types, thus reducing the analysis time of failures, improving coverage, and increasing productivity.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Networked and Distributed Computing
Volume-Issue
8 - 1
Pages
41 - 48
Publication Date
2019/12
ISSN (Online)
2211-7946
ISSN (Print)
2211-7938
DOI
https://doi.org/10.2991/ijndc.k.191204.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Saeed Abdel Wahab
AU  - Reem El Adawi
AU  - Ahmed Khater
PY  - 2019
DA  - 2019/12
TI  - Machine Learning in Failure Regions Detection and Parameters Analysis
JO  - International Journal of Networked and Distributed Computing
SP  - 41
EP  - 48
VL  - 8
IS  - 1
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.k.191204.001
DO  - https://doi.org/10.2991/ijndc.k.191204.001
ID  - Wahab2019
ER  -