Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
State-Based Model Checking of Event-Driven System Requirements
IEEE Transactions on Software Engineering
Program Synthesis from Formal Requirements Specifications Using APTS
Higher-Order and Symbolic Computation
RETNA: From Requirements to Testing in a Natural Way
RE '04 Proceedings of the Requirements Engineering Conference, 12th IEEE International
Is mutation an appropriate tool for testing experiments?
Proceedings of the 27th international conference on Software engineering
MuJava: an automated class mutation system: Research Articles
Software Testing, Verification & Reliability
Producing a Global Requirement Model from Multiple Requirement Specifications
EDOC '07 Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference
Testing against Natural Language Requirements
QSIC '07 Proceedings of the Seventh International Conference on Quality Software
Generating Test Cases for Timed Systems from Controlled Natural Language Specifications
SSIRI '09 Proceedings of the 2009 Third IEEE International Conference on Secure Software Integration and Reliability Improvement
A real-world benchmark model for testing concurrent real-time systems in the automotive domain
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
Generating model-based test cases from natural language requirements for space application software
Software Quality Control
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Formal models are increasingly used as input for automated test generation strategies. As an example, Software Cost Reduction (SCR) has been designed to detect and correct errors during the requirements phase, also allowing test generation. However, SCR syntax is not trivial for those who are unfamiliar with it. We propose here a strategy to generate test cases from natural language requirements using SCR as an intermediate and hidden formalism. To avoid textual ambiguity, the requirements are written according to a Controlled Natural Language. Each syntactically valid requirement is mapped into a semantic representation from which an SCR specification is derived. We then use the T-VEC tool to generate tests from SCR. We evaluated our strategy based on requirements and manually written test vectors provided by our partner from the Aviation Industry. Our strategy generated 85% of the vectors in the original set, with 100% of precision. The generation time was 2s. Yet, we obtained a mutation score of 84%.