State Model Inference Through the GUI Using Run-Time Test Generation

Ad Mulders; Olivia Rodriguez Valdes; Fernando Pastor Ricós; Pekka Aho; Beatriz Marín; Tanja E. J. Vos

doi:10.1007/978-3-031-05760-1_32

State Model Inference Through the GUI Using Run-Time Test Generation

Ad Mulders
, Olivia Rodriguez Valdes
, Fernando Pastor Ricós
, Pekka Aho^*
, Beatriz Marín
, Tanja E. J. Vos

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Abstract

Software testing is an important part of engineering trustworthy information systems. End-to-end testing through Graphical User Interface (GUI) can be done manually, but it is a very time consuming and costly process. There are tools to capture or manually define scripts for automating regression testing through a GUI, but the main challenge is the high maintenance cost of the scripts when the GUI changes. In addition, GUIs tend to have a large state space, so creating scripts to cover all the possible paths and defining test oracles to check all the elements of all the states would be an enormous effort. This paper presents an approach to automatically explore a GUI while inferring state models that are used for action selection in run-time GUI test generation, implemented as an extension to the open source TESTAR tool. As an initial validation, we experiment on the impact of using various state abstraction mechanisms on the model inference and the performance of the implemented action selection algorithm based on the inferred model. Later, we analyse the challenges and provide future research directions on model inference and scriptless GUI testing.

Original language	English
Title of host publication	Research Challenges in Information Science
Subtitle of host publication	16th International Conference, RCIS 2022, Barcelona, Spain, May 17–20, 2022, Proceedings
Editors	Renata Guizzardi, Jolita Ralyté, Xavier Franch
Place of Publication	Cham
Publisher	Springer
Pages	546-563
Number of pages	18
Edition	1
ISBN (Print)	9783031057595
DOIs	https://doi.org/10.1007/978-3-031-05760-1_32
Publication status	Published - 13 May 2022
Event	16th International Conference on Research Challenges in Information Science - Barcelona, Spain Duration: 17 May 2022 → 20 May 2022 https://www.rcis-conf.com/rcis2022/

Publication series

Series	Lecture Notes in Business Information Processing
Volume	446
ISSN	1865-1348

Conference

Conference	16th International Conference on Research Challenges in Information Science
Abbreviated title	RCIS 2022
Country/Territory	Spain
City	Barcelona
Period	17/05/22 → 20/05/22
Internet address	https://www.rcis-conf.com/rcis2022/

Keywords

Automated GUI testing
Model inference
TESTAR tool

Access to Document

10.1007/978-3-031-05760-1_32

Cite this

Mulders, A., Valdes, O. R., Ricós, F. P., Aho, P., Marín, B., & Vos, T. E. J. (2022). State Model Inference Through the GUI Using Run-Time Test Generation. In R. Guizzardi, J. Ralyté, & X. Franch (Eds.), Research Challenges in Information Science: 16th International Conference, RCIS 2022, Barcelona, Spain, May 17–20, 2022, Proceedings (1 ed., pp. 546-563). Springer. https://doi.org/10.1007/978-3-031-05760-1_32

Mulders, Ad ; Valdes, Olivia Rodriguez ; Ricós, Fernando Pastor et al. / State Model Inference Through the GUI Using Run-Time Test Generation. Research Challenges in Information Science: 16th International Conference, RCIS 2022, Barcelona, Spain, May 17–20, 2022, Proceedings. editor / Renata Guizzardi ; Jolita Ralyté ; Xavier Franch. 1. ed. Cham : Springer, 2022. pp. 546-563 (Lecture Notes in Business Information Processing, Vol. 446).

@inproceedings{cf1c03ae84eb4918bbac21d809b796e0,

title = "State Model Inference Through the GUI Using Run-Time Test Generation",

abstract = "Software testing is an important part of engineering trustworthy information systems. End-to-end testing through Graphical User Interface (GUI) can be done manually, but it is a very time consuming and costly process. There are tools to capture or manually define scripts for automating regression testing through a GUI, but the main challenge is the high maintenance cost of the scripts when the GUI changes. In addition, GUIs tend to have a large state space, so creating scripts to cover all the possible paths and defining test oracles to check all the elements of all the states would be an enormous effort. This paper presents an approach to automatically explore a GUI while inferring state models that are used for action selection in run-time GUI test generation, implemented as an extension to the open source TESTAR tool. As an initial validation, we experiment on the impact of using various state abstraction mechanisms on the model inference and the performance of the implemented action selection algorithm based on the inferred model. Later, we analyse the challenges and provide future research directions on model inference and scriptless GUI testing.",

keywords = "Automated GUI testing, Model inference, TESTAR tool",

author = "Ad Mulders and Valdes, \{Olivia Rodriguez\} and Ric{\'o}s, \{Fernando Pastor\} and Pekka Aho and Beatriz Mar{\'i}n and Vos, \{Tanja E. J.\}",

note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.; 16th International Conference on Research Challenges in Information Science, RCIS 2022 ; Conference date: 17-05-2022 Through 20-05-2022",

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month = may,

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doi = "10.1007/978-3-031-05760-1\_32",

language = "English",

isbn = "9783031057595",

series = "Lecture Notes in Business Information Processing",

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editor = "Renata Guizzardi and Jolita Ralyt{\'e} and Xavier Franch",

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Mulders, A, Valdes, OR, Ricós, FP, Aho, P, Marín, B & Vos, TEJ 2022, State Model Inference Through the GUI Using Run-Time Test Generation. in R Guizzardi, J Ralyté & X Franch (eds), Research Challenges in Information Science: 16th International Conference, RCIS 2022, Barcelona, Spain, May 17–20, 2022, Proceedings. 1 edn, Springer, Cham, Lecture Notes in Business Information Processing, vol. 446, pp. 546-563, 16th International Conference on Research Challenges in Information Science, Barcelona, Spain, 17/05/22. https://doi.org/10.1007/978-3-031-05760-1_32

State Model Inference Through the GUI Using Run-Time Test Generation. / Mulders, Ad; Valdes, Olivia Rodriguez; Ricós, Fernando Pastor et al.
Research Challenges in Information Science: 16th International Conference, RCIS 2022, Barcelona, Spain, May 17–20, 2022, Proceedings. ed. / Renata Guizzardi; Jolita Ralyté; Xavier Franch. 1. ed. Cham: Springer, 2022. p. 546-563 (Lecture Notes in Business Information Processing, Vol. 446).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - State Model Inference Through the GUI Using Run-Time Test Generation

AU - Mulders, Ad

AU - Valdes, Olivia Rodriguez

AU - Ricós, Fernando Pastor

AU - Aho, Pekka

AU - Marín, Beatriz

AU - Vos, Tanja E. J.

N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2022/5/13

Y1 - 2022/5/13

N2 - Software testing is an important part of engineering trustworthy information systems. End-to-end testing through Graphical User Interface (GUI) can be done manually, but it is a very time consuming and costly process. There are tools to capture or manually define scripts for automating regression testing through a GUI, but the main challenge is the high maintenance cost of the scripts when the GUI changes. In addition, GUIs tend to have a large state space, so creating scripts to cover all the possible paths and defining test oracles to check all the elements of all the states would be an enormous effort. This paper presents an approach to automatically explore a GUI while inferring state models that are used for action selection in run-time GUI test generation, implemented as an extension to the open source TESTAR tool. As an initial validation, we experiment on the impact of using various state abstraction mechanisms on the model inference and the performance of the implemented action selection algorithm based on the inferred model. Later, we analyse the challenges and provide future research directions on model inference and scriptless GUI testing.

AB - Software testing is an important part of engineering trustworthy information systems. End-to-end testing through Graphical User Interface (GUI) can be done manually, but it is a very time consuming and costly process. There are tools to capture or manually define scripts for automating regression testing through a GUI, but the main challenge is the high maintenance cost of the scripts when the GUI changes. In addition, GUIs tend to have a large state space, so creating scripts to cover all the possible paths and defining test oracles to check all the elements of all the states would be an enormous effort. This paper presents an approach to automatically explore a GUI while inferring state models that are used for action selection in run-time GUI test generation, implemented as an extension to the open source TESTAR tool. As an initial validation, we experiment on the impact of using various state abstraction mechanisms on the model inference and the performance of the implemented action selection algorithm based on the inferred model. Later, we analyse the challenges and provide future research directions on model inference and scriptless GUI testing.

KW - Automated GUI testing

KW - Model inference

KW - TESTAR tool

U2 - 10.1007/978-3-031-05760-1_32

DO - 10.1007/978-3-031-05760-1_32

M3 - Conference Article in proceeding

SN - 9783031057595

T3 - Lecture Notes in Business Information Processing

SP - 546

EP - 563

BT - Research Challenges in Information Science

A2 - Guizzardi, Renata

A2 - Ralyté, Jolita

A2 - Franch, Xavier

PB - Springer

CY - Cham

T2 - 16th International Conference on Research Challenges in Information Science

Y2 - 17 May 2022 through 20 May 2022

ER -

Mulders A, Valdes OR, Ricós FP, Aho P, Marín B, Vos TEJ. State Model Inference Through the GUI Using Run-Time Test Generation. In Guizzardi R, Ralyté J, Franch X, editors, Research Challenges in Information Science: 16th International Conference, RCIS 2022, Barcelona, Spain, May 17–20, 2022, Proceedings. 1 ed. Cham: Springer. 2022. p. 546-563. (Lecture Notes in Business Information Processing, Vol. 446). doi: 10.1007/978-3-031-05760-1_32

State Model Inference Through the GUI Using Run-Time Test Generation

Abstract

Publication series

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Keywords

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