TY - GEN
T1 - MUPPAAL
T2 - 16th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023
AU - CUARTAS GRANADA, Jaime
AU - ARANDA BUENO, Jesus Alexander
AU - Cordy, Maxime
AU - Ortiz Vega, James Jerson
AU - Perrouin, Gilles
AU - Schobbens, Pierre-Yves
N1 - Funding Information:
Gilles Perrouin is an FNRS (Fonds National de la Recherche Scientifique) Research Associate. Jaime Cuartas received support from ERASMUS+ while at the University of Namur. Maxime Cordy obtained funding from FNR Luxembourg (grant INTER/FNRS/20/15077233/Scaling Up Variability/ Cordy). Work partially funded by ERDF project IDEES. We thank Paul Temple for the early discussions on this work.
Funding Information:
ACKNOWLEDGMENT Gilles Perrouin is an FNRS (Fonds National de la Recherche Scientifique) Research Associate. Jaime Cuartas received support from ERASMUS+ while at the University of Namur. Maxime Cordy obtained funding from FNR Luxembourg (grant INTER/FNRS/20/15077233/Scaling Up Variability/Cordy). Work partially funded by ERDF project IDEES. We thank Paul Temple for the early discussions on this work.
Publisher Copyright:
© 2023 IEEE.
PY - 2023/4/16
Y1 - 2023/4/16
N2 - Mutation Testing (MT) is a test quality assessment technique that creates mutants by injecting artificial faults into the system and evaluating the ability of tests to distinguish these mutants. We focus on MT for safety-critical Timed Automata (TA). MT is prone to equivalent and duplicate mutants, the former having the same behaviour as the original system and the latter other mutants. Such mutants bring no value and induce useless test case executions. We propose MUPPAAL, a tool that: (1) offers a new operator reducing the occurrence of mutant duplicates; (2) an efficient bisimulation algorithm removing remaining duplicates; (3) leverages existing equivalence-avoiding mutation operators. Our experiments on four UPPAAL case studies indicate that duplicates represent up to 32% of all mutants and that the MUPPAAL bisimulation algorithm can identify them more than 99% of the time.
AB - Mutation Testing (MT) is a test quality assessment technique that creates mutants by injecting artificial faults into the system and evaluating the ability of tests to distinguish these mutants. We focus on MT for safety-critical Timed Automata (TA). MT is prone to equivalent and duplicate mutants, the former having the same behaviour as the original system and the latter other mutants. Such mutants bring no value and induce useless test case executions. We propose MUPPAAL, a tool that: (1) offers a new operator reducing the occurrence of mutant duplicates; (2) an efficient bisimulation algorithm removing remaining duplicates; (3) leverages existing equivalence-avoiding mutation operators. Our experiments on four UPPAAL case studies indicate that duplicates represent up to 32% of all mutants and that the MUPPAAL bisimulation algorithm can identify them more than 99% of the time.
KW - Model-Based Testing
KW - Mutation Testing
KW - Timed Automata
KW - UPPAAL
UR - http://www.scopus.com/inward/record.url?scp=85163130733&partnerID=8YFLogxK
U2 - 10.1109/icstw58534.2023.00021
DO - 10.1109/icstw58534.2023.00021
M3 - Conference contribution
AN - SCOPUS:85163130733
T3 - 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)
SP - 52
EP - 61
BT - Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 April 2023 through 20 April 2023
ER -