To Detect Abnormal Program Behaviours via Mutation Deduction

Jie Zhang; Dan Hao; Lingming Zhang; Lu Zhang

doi:10.1109/ICSTW.2018.00022

To Detect Abnormal Program Behaviours via Mutation Deduction

Jie Zhang, Dan Hao, Lingming Zhang, Lu Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents Mutation Deduction, a methodology framework that explores the behaviours of a program under test through exploring the behaviours of its mutants. Mutation Deduction makes use of the relationship between the syntactic changes and semantic changes of a program under test: a new type of metamorphic relation. A metamorphic? relation is supposed to hold as long as the program is correct. Once a metamorphic relation is violated, one could deem that abnormal behaviours exist. The framework is supposed to apply to both single-version and multi-version scenarios. It enlarges the behaviour space that the current test inputs could explore, which could help observe more abnormal behaviours and better ensure software quality.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	11-17
Number of pages	7
ISBN (Electronic)	9781538663523
DOIs	https://doi.org/10.1109/ICSTW.2018.00022
State	Published - Jul 16 2018
Externally published	Yes
Event	11th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018 - Vasteras, Sweden Duration: Apr 9 2018 → Apr 13 2018

Publication series

Name	Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018

Conference

Conference	11th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018
Country/Territory	Sweden
City	Vasteras
Period	4/9/18 → 4/13/18

Keywords

Mutation deduction
Mutation testing
Program behaviour exploration

ASJC Scopus subject areas

Software
Safety, Risk, Reliability and Quality

Online availability

10.1109/ICSTW.2018.00022

Library availability

Discover UIUC Full Text

Cite this

Zhang, J., Hao, D., Zhang, L., & Zhang, L. (2018). To Detect Abnormal Program Behaviours via Mutation Deduction. In Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018 (pp. 11-17). (Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSTW.2018.00022

To Detect Abnormal Program Behaviours via Mutation Deduction. / Zhang, Jie; Hao, Dan; Zhang, Lingming et al.
Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 11-17 (Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, J, Hao, D, Zhang, L & Zhang, L 2018, To Detect Abnormal Program Behaviours via Mutation Deduction. in Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018. Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018, Institute of Electrical and Electronics Engineers Inc., pp. 11-17, 11th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018, Vasteras, Sweden, 4/9/18. https://doi.org/10.1109/ICSTW.2018.00022

Zhang J, Hao D, Zhang L, Zhang L. To Detect Abnormal Program Behaviours via Mutation Deduction. In Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 11-17. (Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018). doi: 10.1109/ICSTW.2018.00022

Zhang, Jie ; Hao, Dan ; Zhang, Lingming et al. / To Detect Abnormal Program Behaviours via Mutation Deduction. Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 11-17 (Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018).

@inproceedings{a60abb1e5dfc443aa357c4e029f040bd,

title = "To Detect Abnormal Program Behaviours via Mutation Deduction",

abstract = "This paper presents Mutation Deduction, a methodology framework that explores the behaviours of a program under test through exploring the behaviours of its mutants. Mutation Deduction makes use of the relationship between the syntactic changes and semantic changes of a program under test: a new type of metamorphic relation. A metamorphic? relation is supposed to hold as long as the program is correct. Once a metamorphic relation is violated, one could deem that abnormal behaviours exist. The framework is supposed to apply to both single-version and multi-version scenarios. It enlarges the behaviour space that the current test inputs could explore, which could help observe more abnormal behaviours and better ensure software quality.",

keywords = "Mutation deduction, Mutation testing, Program behaviour exploration",

author = "Jie Zhang and Dan Hao and Lingming Zhang and Lu Zhang",

note = "ACKNOWLEDGMENTS This work is mainly supported by the National Key Research and Development Program 2016YFB1000801, the National Natural Science Foundation of China under Grants No. 61522201 and 61529201. It is also supported in part by NSF Grant No. CCF-1566589, UT Dallas faculty start-up fund, Google Faculty Research Award. During the process of this research that took place while at UCL full time, Mark Harman and Yue Jia were part funded by the EPSRC grant DAASE Dynamic Adaptive Automated Software Engineering (EP/J017515/).; 11th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018 ; Conference date: 09-04-2018 Through 13-04-2018",

year = "2018",

month = jul,

day = "16",

doi = "10.1109/ICSTW.2018.00022",

language = "English (US)",

series = "Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "11--17",

booktitle = "Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018",

address = "United States",

}

TY - GEN

T1 - To Detect Abnormal Program Behaviours via Mutation Deduction

AU - Zhang, Jie

AU - Hao, Dan

AU - Zhang, Lingming

AU - Zhang, Lu

N1 - ACKNOWLEDGMENTS This work is mainly supported by the National Key Research and Development Program 2016YFB1000801, the National Natural Science Foundation of China under Grants No. 61522201 and 61529201. It is also supported in part by NSF Grant No. CCF-1566589, UT Dallas faculty start-up fund, Google Faculty Research Award. During the process of this research that took place while at UCL full time, Mark Harman and Yue Jia were part funded by the EPSRC grant DAASE Dynamic Adaptive Automated Software Engineering (EP/J017515/).

PY - 2018/7/16

Y1 - 2018/7/16

N2 - This paper presents Mutation Deduction, a methodology framework that explores the behaviours of a program under test through exploring the behaviours of its mutants. Mutation Deduction makes use of the relationship between the syntactic changes and semantic changes of a program under test: a new type of metamorphic relation. A metamorphic? relation is supposed to hold as long as the program is correct. Once a metamorphic relation is violated, one could deem that abnormal behaviours exist. The framework is supposed to apply to both single-version and multi-version scenarios. It enlarges the behaviour space that the current test inputs could explore, which could help observe more abnormal behaviours and better ensure software quality.

AB - This paper presents Mutation Deduction, a methodology framework that explores the behaviours of a program under test through exploring the behaviours of its mutants. Mutation Deduction makes use of the relationship between the syntactic changes and semantic changes of a program under test: a new type of metamorphic relation. A metamorphic? relation is supposed to hold as long as the program is correct. Once a metamorphic relation is violated, one could deem that abnormal behaviours exist. The framework is supposed to apply to both single-version and multi-version scenarios. It enlarges the behaviour space that the current test inputs could explore, which could help observe more abnormal behaviours and better ensure software quality.

KW - Mutation deduction

KW - Mutation testing

KW - Program behaviour exploration

UR - http://www.scopus.com/inward/record.url?scp=85049050248&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049050248&partnerID=8YFLogxK

U2 - 10.1109/ICSTW.2018.00022

DO - 10.1109/ICSTW.2018.00022

M3 - Conference contribution

AN - SCOPUS:85049050248

T3 - Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018

SP - 11

EP - 17

BT - Proceedings - 2018 IEEE 11th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 11th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018

Y2 - 9 April 2018 through 13 April 2018

ER -

To Detect Abnormal Program Behaviours via Mutation Deduction

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Cite this