Patterns in property specifications for finite-state verification
Proceedings of the 21st international conference on Software engineering
PROPEL: an approach supporting property elucidation
Proceedings of the 24th International Conference on Software Engineering
Improving Requirements Tracing via Information Retrieval
RE '03 Proceedings of the 11th IEEE International Conference on Requirements Engineering
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Helping Analysts Trace Requirements: An Objective Look
RE '04 Proceedings of the Requirements Engineering Conference, 12th IEEE International
Classifying Requirements: Towards a More Rigorous Analysis of Natural-Language Specifications
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
User guidance for creating precise and accessible property specifications
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Spin model checker, the: primer and reference manual
Spin model checker, the: primer and reference manual
A clustering-based approach for discovering flaws in requirements specifications
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Simulation validation using the compatibility between simulation model and experimental frame
Proceedings of the 2013 Summer Computer Simulation Conference
| Hi-index | 0.00 |
Analyzing requirements for consistency and checking them for correctness can require significant effort, particularly if they have not been maintained with a requirements management tool (e.g., DOORS) or specified in a machine-readable notation. By restricting the number of requirements being analyzed, fewer opportunities exist for introducing errors into the analysis. This can be accomplished by subsetting the requirements and analyzing one subset at a time. Previous work showed that simple natural language processing and machine learning techniques can be used to identify temporal requirements within a set of natural language requirements. This paper builds on that work by detailing our results in applying these techniques to a set of natural-language temporal requirements taken from a current JPL mission and determining whether a requirement is one of the most frequently occurring types of temporal requirements. The ability to distinguish between different LTL patterns in natural-language requirements raises the possibility of automating the transformation of natural-language temporal requirements into LTL expressions. This would allow automated consistency checking and tracing of natural-language temporal requirements. Since correctness properties are often specified as LTL expressions, this would also provide a set of correctness properties against which abstract models of the system could be verified.