Using “test model-checking” to verify the Runway-PA8000 memory model

1998 ◽  
Author(s):  
Rajnish Ghughal ◽  
Abdel Mokkedem ◽  
Ratan Nalumasu ◽  
Ganesh Gopalakrishnan
Keyword(s):  
Model Checking ◽  
Memory Model ◽  
Test Model

Requirements Traceability within Model-Based Testing

2011 ◽  
Vol 2 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Vanessa Grosch
Keyword(s):  
Model Checking ◽  
Graphical Representation ◽  
Timed Automata ◽  
Test Cases ◽  
Test Model ◽  
Requirements Traceability ◽  
Formal Approach ◽  
Model Based ◽  
State Charts ◽  
Model Based Testing

Requirements traceability enables the linkage between all development artifacts during the development process. Within model-based testing, requirements traceability links the original requirements with test model elements and generated test cases. Current approaches are either not practical or lack the necessary formal foundation for generating requirements-based test cases using model-checking techniques involving the requirements trace. This paper describes a practical and formal approach to ensure requirements traceability. The descriptions of the requirements are defined on path fragments of timed automata or timed state charts. The graphical representation of these paths is called a computation sequence chart (CSC). CSCs are automatically transformed into temporal logic formulae. A model-checking algorithm considers these formulae when generating test cases.

scholarly journals Improvement of a Library for Model Checking under Weakly Ordered Memory Model with SPIN

2018 ◽  
Vol 26 (0) ◽  
pp. 314-326
Author(s):  
Kosuke Matsumoto ◽  
Tomoharu Ugawa ◽  
Tatsuya Abe
Keyword(s):  
Model Checking ◽  
Memory Model

scholarly journals Test model coverage analysis under uncertainty: extended version

2021 ◽  
Author(s):  
I. S. W. B. Prasetya ◽  
Rick Klomp
Keyword(s):  
Model Checking ◽  
Deterministic Model ◽  
Probabilistic Model Checking ◽  
Test Case ◽  
Test Model ◽  
Extended Version ◽  
Efficient Calculation ◽  
Model Based Testing ◽  
Coverage Analysis ◽  
Probabilistic Coverage

AbstractIn model-based testing, 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 also of probabilistic aggregate coverage in combination with k-wise coverage.

scholarly journals Dartagnan: Bounded Model Checking for Weak Memory Models (Competition Contribution)

2020 ◽  
pp. 378-382
Author(s):  
Hernán Ponce-de-León ◽  
Florian Furbach ◽  
Keijo Heljanko ◽  
Roland Meyer
Keyword(s):  
Model Checking ◽  
Bounded Model Checking ◽  
Memory Models ◽  
Memory Model ◽  
Complete Analysis ◽  
Concurrent Programs ◽  
Sequential Consistency ◽  
Model Checker ◽  
Safety Properties ◽  
C Programs

Abstract Dartagnanis a bounded model checker for concurrent programs under weak memory models. What makes it different from other tools is that the memory model is not hard-coded inside Dartagnanbut taken as part of the input. For SV-COMP’20, we take as input sequential consistency (i.e. the standard interleaving memory model) extended by support for atomic blocks. Our point is to demonstrate that a universal tool can be competitive and perform well in SV-COMP. Being a bounded model checker, Dartagnan’s focus is on disproving safety properties by finding counterexample executions. For programs with bounded loops, Dartagnanperforms an iterative unwinding that results in a complete analysis. The SV-COMP’20 version of Dartagnanworks on Boogiecode. The C programs of the competition are translated internally to Boogieusing SMACK.

Parameterized Model Checking on the TSO Weak Memory Model

2020 ◽  
Vol 64 (7) ◽  
pp. 1307-1330
Author(s):  
Sylvain Conchon ◽  
David Declerck ◽  
Fatiha Zaïdi
Keyword(s):  
Model Checking ◽  
Memory Model ◽  
Parameterized Model
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