Back to Search

Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems

Title	Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems
Author	Kıraç, Mustafa Furkan, Aktemur, Tankut Barış, Sözer, Hasan, Gebizli, C. Ş.
Publication Date:	2019-06
Publication Place	- The ACM Digital Library
Subject	Test case generation, Black-box testing, Recurrent neural networks, Long short-term memory networks, Learning usage behavior
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0963-9314
Record ID	40a3ff0b-b5a5-4fd3-8f02-bfc07cb0df8f
Library Location	Computer Science
Date	2019-06
Sample Text	We propose a novel technique based on recurrent artificial neural networks to generate test cases for black-box testing of reactive systems. We combine functional testing inputs that are automatically generated from a model together with manually-applied test cases for robustness testing. We use this combination to train a long short-term memory (LSTM) network. As a result, the network learns an implicit representation of the usage behavior that is liable to failures. We use this network to generate new event sequences as test cases. We applied our approach in the context of an industrial case study for the black-box testing of a digital TV system. LSTM-generated test cases were able to reveal several faults, including critical ones, that were not detected with existing automated or manual testing activities. Our approach is complementary to model-based and exploratory testing, and the combined approach outperforms random testing in terms of both fault coverage and execution time.
DOI	10.1007/s11219-018-9439-1
Cilt	27

View in source Özyeğin University Özyeğin University - Historical works, archives, and periodicals search engine

Özyeğin University

Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems

Author Kıraç, Mustafa Furkan, Aktemur, Tankut Barış, Sözer, Hasan, Gebizli, C. Ş.

Publication Date 2019-06

Publication Place - The ACM Digital Library

Subject Test case generation, Black-box testing, Recurrent neural networks, Long short-term memory networks, Learning usage behavior

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0963-9314

Record ID 40a3ff0b-b5a5-4fd3-8f02-bfc07cb0df8f

Library Location Computer Science

Date 2019-06

Sample Text We propose a novel technique based on recurrent artificial neural networks to generate test cases for black-box testing of reactive systems. We combine functional testing inputs that are automatically generated from a model together with manually-applied test cases for robustness testing. We use this combination to train a long short-term memory (LSTM) network. As a result, the network learns an implicit representation of the usage behavior that is liable to failures. We use this network to generate new event sequences as test cases. We applied our approach in the context of an industrial case study for the black-box testing of a digital TV system. LSTM-generated test cases were able to reveal several faults, including critical ones, that were not detected with existing automated or manual testing activities. Our approach is complementary to model-based and exploratory testing, and the combined approach outperforms random testing in terms of both fault coverage and execution time.

DOI 10.1007/s11219-018-9439-1

Cilt 27