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

Name: 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.

İsim	Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems
Yazar	Kıraç, Mustafa Furkan, Aktemur, Tankut Barış, Sözer, Hasan, Gebizli, C. Ş.
Basım Tarihi:	2019-06
Basım Yeri	- The ACM Digital Library
Konu	Test case generation, Black-box testing, Recurrent neural networks, Long short-term memory networks, Learning usage behavior
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0963-9314
Kayıt Numarası	40a3ff0b-b5a5-4fd3-8f02-bfc07cb0df8f
Lokasyon	Computer Science
Tarih	2019-06
Örnek Metin	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

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

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

Basım Tarihi 2019-06

Basım Yeri - The ACM Digital Library

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0963-9314

Kayıt Numarası 40a3ff0b-b5a5-4fd3-8f02-bfc07cb0df8f

Lokasyon Computer Science

Tarih 2019-06

Örnek Metin 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