Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding

Ege Apak; Ayse Tosun

doi:10.1145/3387940.3392216

Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding

Ege Apak, Ayse Tosun

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

Load test is conducted in order to gain an insight to the characteristics of a system under various amount of load. Since the combination of possible actions a user can follow from start to finish is possibly endless, the possibility of missing a load inducing scenario by using a traditional load testing software is highly probable. In this work, we implement a rule-aided scenario generation algorithm and find the possible scenarios that a high amount of load is generated by using genetic algorithms to drive the search forward.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020
Yayınlayan	Association for Computing Machinery, Inc
Sayfalar	533-536
Sayfa sayısı	4
ISBN (Elektronik)	9781450379632
DOI'lar	https://doi.org/10.1145/3387940.3392216
Yayın durumu	Yayınlandı - 27 Haz 2020
Etkinlik	42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020 - Seoul, Korea, Republic of Süre: 27 Haz 2020 → 19 Tem 2020

Yayın serisi

Adı	Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020
Ülke/Bölge	Korea, Republic of
Şehir	Seoul
Periyot	27/06/20 → 19/07/20

Bibliyografik not

Publisher Copyright:
© 2020 ACM.

Belgeye Erişim

10.1145/3387940.3392216

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Apak, E., & Tosun, A. (2020). Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding. In Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020 (pp. 533-536). (Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020). Association for Computing Machinery, Inc. https://doi.org/10.1145/3387940.3392216

Apak, Ege ; Tosun, Ayse. / Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding. Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020. Association for Computing Machinery, Inc, 2020. pp. 533-536 (Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020).

@inproceedings{026cddcae2a7492092b31e9ec17b1401,

title = "Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding",

abstract = "Load test is conducted in order to gain an insight to the characteristics of a system under various amount of load. Since the combination of possible actions a user can follow from start to finish is possibly endless, the possibility of missing a load inducing scenario by using a traditional load testing software is highly probable. In this work, we implement a rule-aided scenario generation algorithm and find the possible scenarios that a high amount of load is generated by using genetic algorithms to drive the search forward.",

keywords = "genetic algorithms, load testing, tree-based encoding",

author = "Ege Apak and Ayse Tosun",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020 ; Conference date: 27-06-2020 Through 19-07-2020",

year = "2020",

month = jun,

day = "27",

doi = "10.1145/3387940.3392216",

language = "English",

series = "Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020",

publisher = "Association for Computing Machinery, Inc",

pages = "533--536",

booktitle = "Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020",

}

Apak, E & Tosun, A 2020, Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding. in Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020. Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020, Association for Computing Machinery, Inc, pp. 533-536, 42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020, Seoul, Korea, Republic of, 27/06/20. https://doi.org/10.1145/3387940.3392216

Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding. / Apak, Ege; Tosun, Ayse.
Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020. Association for Computing Machinery, Inc, 2020. p. 533-536 (Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding

AU - Apak, Ege

AU - Tosun, Ayse

PY - 2020/6/27

Y1 - 2020/6/27

N2 - Load test is conducted in order to gain an insight to the characteristics of a system under various amount of load. Since the combination of possible actions a user can follow from start to finish is possibly endless, the possibility of missing a load inducing scenario by using a traditional load testing software is highly probable. In this work, we implement a rule-aided scenario generation algorithm and find the possible scenarios that a high amount of load is generated by using genetic algorithms to drive the search forward.

AB - Load test is conducted in order to gain an insight to the characteristics of a system under various amount of load. Since the combination of possible actions a user can follow from start to finish is possibly endless, the possibility of missing a load inducing scenario by using a traditional load testing software is highly probable. In this work, we implement a rule-aided scenario generation algorithm and find the possible scenarios that a high amount of load is generated by using genetic algorithms to drive the search forward.

KW - genetic algorithms

KW - load testing

KW - tree-based encoding

UR - http://www.scopus.com/inward/record.url?scp=85093113461&partnerID=8YFLogxK

U2 - 10.1145/3387940.3392216

DO - 10.1145/3387940.3392216

M3 - Conference contribution

AN - SCOPUS:85093113461

T3 - Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020

SP - 533

EP - 536

BT - Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020

PB - Association for Computing Machinery, Inc

T2 - 42nd IEEE/ACM International Conference on Software Engineering Workshops, ICSEW 2020

Y2 - 27 June 2020 through 19 July 2020

ER -

Apak E, Tosun A. Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding. In Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020. Association for Computing Machinery, Inc. 2020. p. 533-536. (Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020). doi: 10.1145/3387940.3392216

Finding Load Inducing Test Scenarios Using Genetic Algorithms and Tree Based Encoding

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap