Machine Learning in Failure Regions Detection and Parameters Analysis

DOI

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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TY  - JOUR
AU  - Saeed Abdel Wahab
AU  - Reem El Adawi
AU  - Ahmed Khater
PY  - 2019
DA  - 2019/12/24
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  - 10.2991/ijndc.k.191204.001
ID  - Wahab2019
ER  -