Automated Identification of LTL Patterns in Natural Language Requirements

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.