Context, Boilerplates have long been used in Requirements Engineering (RE) to increase the precision of natural language requirements and to avoid ambiguity problems caused by unrestricted natural language. When boilerplates are used, an important quality assurance task is to verify that the requirements indeed conform to the boilerplates. Objective. If done manually, checking conformance to boilerplates is laborious, presenting a particular challenge when the task has to be repeated multiple times in response to requirements changes. Our objective is to provide automation for checking conformance to boilerplates using a Natural Language Processing (NLP) technique, called Text Chunking, and to empirically validate the effectiveness of the automation. Method. We use an exploratory case study, conducted in an industrial setting, as the basis for our empirical investigation. Results. We present a generalizable and tool-supported approach for boilerplate conformance checking. We report on the application of our approach to the requirements document for a major software component in the satellite domain. We compare alternative text chunking solutions and argue about their effectiveness for boilerplate conformance checking. Conclusion. Our results indicate that: (1) text chunking provides a robust and accurate basis for checking conformance to boilerplates, and (2) the effectiveness of boilerplate conformance checking based on text chunking is not compromised even when the requirements glossary terms are unknown. This makes our work particularly relevant to practice, as many industrial requirements documents have incomplete glossaries.

Using requirement boilerplates is an effective way to mit- igate many types of ambiguity in Natural Language (NL) requirements and to enable more automated transformation and analysis of these requirements. When requirements are expressed using boilerplates, one must check, as a first qual- ity assurance measure, whether the requirements actually conform to the boilerplates. If done manually, boilerplate conformance checking can be laborious, particularly when requirements change frequently. We present RUBRIC (Re- qUirements BoileRplate sanIty Checker), a flexible tool for automatically checking NL requirements against boilerplates for conformance. RUBRIC further provides a range of di- agnostics to highlight potentially problematic syntactic con- structs in NL requirement statements. RUBRIC is based on a Natural Language Processing (NLP) technique, known as text chunking. A key advantage of RUBRIC is that it yields highly accurate results even in early stages of requirements writing, where a requirements glossary may be unavailable or only partially specified. RUBRIC is scalable and can be applied repeatedly to large sets of requirements as they evolve. The tool has been validated through an industrial case study which we outline briefly in the paper.