TY - GEN
T1 - Test Model Coverage Analysis Under Uncertainty
AU - Prasetya, I. S.W.B.
AU - Klomp, Rick
PY - 2019/9/11
Y1 - 2019/9/11
N2 - In model-based testing (MBT) we may have to deal with a non-deterministic model, e.g. because abstraction was applied, or because the software under test itself is non-deterministic. The same test case may then trigger multiple possible execution paths, depending on some internal decisions made by the software. Consequently, performing precise test analyses, e.g. to calculate the test coverage, are not possible. This can be mitigated if developers can annotate the model with estimated probabilities for taking each transition. A probabilistic model checking algorithm can subsequently be used to do simple probabilistic coverage analysis. However, in practice developers often want to know what the achieved aggregate coverage is, which unfortunately cannot be re-expressed as a standard model checking problem. This paper presents an extension to allow efficient calculation of probabilistic aggregate coverage, and moreover also in combination with k-wise coverage.
AB - In model-based testing (MBT) we may have to deal with a non-deterministic model, e.g. because abstraction was applied, or because the software under test itself is non-deterministic. The same test case may then trigger multiple possible execution paths, depending on some internal decisions made by the software. Consequently, performing precise test analyses, e.g. to calculate the test coverage, are not possible. This can be mitigated if developers can annotate the model with estimated probabilities for taking each transition. A probabilistic model checking algorithm can subsequently be used to do simple probabilistic coverage analysis. However, in practice developers often want to know what the achieved aggregate coverage is, which unfortunately cannot be re-expressed as a standard model checking problem. This paper presents an extension to allow efficient calculation of probabilistic aggregate coverage, and moreover also in combination with k-wise coverage.
KW - Probabilistic model based testing
KW - Probabilistic test coverage
KW - Testing non-deterministic systems
UR - https://www.scopus.com/pages/publications/85072865612
U2 - 10.1007/978-3-030-30446-1_12
DO - 10.1007/978-3-030-30446-1_12
M3 - Conference contribution
AN - SCOPUS:85072865612
SN - 9783030304454
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 222
EP - 239
BT - Software Engineering and Formal Methods - 17th International Conference, SEFM 2019, Proceedings
A2 - Ölveczky, Peter Csaba
A2 - Salaün, Gwen
PB - Springer
T2 - 17th International Conference on Software Engineering and Formal Methods, SEFM 2019
Y2 - 18 September 2019 through 20 September 2019
ER -