Well-informed Test Case Generation and Crash Reproduction

Pouria Derakhshanfar

doi:10.1109/ICST46399.2020.00054

Well-informed Test Case Generation and Crash Reproduction

Pouria Derakhshanfar^*

^*Corresponding author for this work

Software Engineering

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

Search-based test data generation approaches have come a long way over the past few years, but these approaches still have some limitations when it comes to exercising specific behavior for triggering particular kinds of faults (e.g., crashes or specific types of integration between classes/modules). In this thesis, we are investigating new fitness functions and evolutionary-based algorithms and techniques to tackle these limitations. We have defined multiple novel approaches for crash reproduction and class integration testing. Currently, we are still working on improving both crash reproduction and class integration testing.

Original language	English
Title of host publication	2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)
Subtitle of host publication	Proceedings
Editors	E. O'Conner
Place of Publication	Piscataway
Publisher	IEEE
Pages	424-426
Number of pages	3
ISBN (Electronic)	978-1-7281-5778-8
ISBN (Print)	978-1-7281-5779-5
DOIs	https://doi.org/10.1109/ICST46399.2020.00054
Publication status	Published - 2020
Event	13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020 - Porto, Portugal Duration: 23 Mar 2020 → 27 Mar 2020

Publication series

Name	2020 IEEE 13TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VALIDATION AND VERIFICATION (ICST 2020)
ISSN (Print)	2381-2834

Conference

Conference	13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020
Country/Territory	Portugal
City	Porto
Period	23/03/20 → 27/03/20

Keywords

automated crash reproduction
automated integration testing
search-based software testing

Access to Document

10.1109/ICST46399.2020.00054

Cite this

Derakhshanfar, P. (2020). Well-informed Test Case Generation and Crash Reproduction. In E. O'Conner (Ed.), 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST): Proceedings (pp. 424-426). [9159102] (2020 IEEE 13TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VALIDATION AND VERIFICATION (ICST 2020)). IEEE . https://doi.org/10.1109/ICST46399.2020.00054

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abstract = "Search-based test data generation approaches have come a long way over the past few years, but these approaches still have some limitations when it comes to exercising specific behavior for triggering particular kinds of faults (e.g., crashes or specific types of integration between classes/modules). In this thesis, we are investigating new fitness functions and evolutionary-based algorithms and techniques to tackle these limitations. We have defined multiple novel approaches for crash reproduction and class integration testing. Currently, we are still working on improving both crash reproduction and class integration testing. ",

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author = "Pouria Derakhshanfar",

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note = "13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020 ; Conference date: 23-03-2020 Through 27-03-2020",

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Derakhshanfar, P 2020, Well-informed Test Case Generation and Crash Reproduction. in E O'Conner (ed.), 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST): Proceedings., 9159102, 2020 IEEE 13TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VALIDATION AND VERIFICATION (ICST 2020), IEEE , Piscataway, pp. 424-426, 13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020, Porto, Portugal, 23/03/20. https://doi.org/10.1109/ICST46399.2020.00054

Well-informed Test Case Generation and Crash Reproduction. / Derakhshanfar, Pouria.

2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST): Proceedings. ed. / E. O'Conner. Piscataway : IEEE , 2020. p. 424-426 9159102 (2020 IEEE 13TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VALIDATION AND VERIFICATION (ICST 2020)).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AB - Search-based test data generation approaches have come a long way over the past few years, but these approaches still have some limitations when it comes to exercising specific behavior for triggering particular kinds of faults (e.g., crashes or specific types of integration between classes/modules). In this thesis, we are investigating new fitness functions and evolutionary-based algorithms and techniques to tackle these limitations. We have defined multiple novel approaches for crash reproduction and class integration testing. Currently, we are still working on improving both crash reproduction and class integration testing.

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Derakhshanfar P. Well-informed Test Case Generation and Crash Reproduction. In O'Conner E, editor, 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST): Proceedings. Piscataway: IEEE . 2020. p. 424-426. 9159102. (2020 IEEE 13TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VALIDATION AND VERIFICATION (ICST 2020)). https://doi.org/10.1109/ICST46399.2020.00054

Well-informed Test Case Generation and Crash Reproduction

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Keywords

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