TY - GEN
T1 - MOTIF
T2 - 17th IEEE Conference on Software Testing, Verification and Validation, ICST 2024
AU - Lee, Jaekwon
AU - Vigano, Enrico
AU - Pastore, Fabrizio
AU - Briand, Lionel
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Mutation testing consists of generating test cases that detect faults injected into software (generating mutants) which its original test suite could not. By running such an augmented set of test cases, it may discover actual faults that may have gone unnoticed with the original test suite. It is thus a desired practice for embedded software running in safety-critical cyber-physical systems (CPS). Unfortunately, the state-of-the-art tool targeting C, a typical language for CPS software, relies on symbolic execution, whose limitations often prevent its application. MOTIF overcomes such limitations by leveraging grey-box fuzzing tools to generate unit test cases in C that detect injected faults in mutants. Indeed, fuzzing tools automatically generate inputs by exercising the compiled version of the software under test guided by coverage feedback, thus overcoming the limitations of symbolic execution. Our empirical assessment has shown that it detects more faults than symbolic execution (i.e., up to 47 percentage points), when the latter is applicable.
AB - Mutation testing consists of generating test cases that detect faults injected into software (generating mutants) which its original test suite could not. By running such an augmented set of test cases, it may discover actual faults that may have gone unnoticed with the original test suite. It is thus a desired practice for embedded software running in safety-critical cyber-physical systems (CPS). Unfortunately, the state-of-the-art tool targeting C, a typical language for CPS software, relies on symbolic execution, whose limitations often prevent its application. MOTIF overcomes such limitations by leveraging grey-box fuzzing tools to generate unit test cases in C that detect injected faults in mutants. Indeed, fuzzing tools automatically generate inputs by exercising the compiled version of the software under test guided by coverage feedback, thus overcoming the limitations of symbolic execution. Our empirical assessment has shown that it detects more faults than symbolic execution (i.e., up to 47 percentage points), when the latter is applicable.
KW - CPS
KW - European Space Agency
KW - Fuzzing
KW - Mutation testing
UR - http://www.scopus.com/inward/record.url?scp=85203846289&partnerID=8YFLogxK
U2 - 10.1109/ICST60714.2024.00052
DO - 10.1109/ICST60714.2024.00052
M3 - Conference contribution
AN - SCOPUS:85203846289
T3 - Proceedings - 2024 IEEE Conference on Software Testing, Verification and Validation, ICST 2024
SP - 451
EP - 453
BT - Proceedings - 2024 IEEE Conference on Software Testing, Verification and Validation, ICST 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 May 2024 through 31 May 2024
ER -