دانلود کتاب Extracting Domain Models from Natural-Language Requirements: Approach and Industrial Evaluation

Domain modeling is an important step in the transition fromnatural-language requirements to precise specifications. Forlarge systems, building a domain model manually is a laborioustask. Several approaches exist to assist engineers withthis task, whereby candidate domain model elements areautomatically extracted using Natural Language Processing(NLP). Despite the existing work on domain model extraction,important facets remain under-explored: (1) there islimited empirical evidence about the usefulness of existingextraction rules (heuristics) when applied in industrial settings;(2) existing extraction rules do not adequately exploitthe natural-language dependencies detected by modern NLPtechnologies; and (3) an important class of rules developedby the information retrieval community for information extractionremains unutilized for building domain models.Motivated by addressing the above limitations, we developa domain model extractor by bringing together existing extractionrules in the software engineering literature, extendingthese rules with complementary rules from the informationretrieval literature, and proposing new rules to betterexploit results obtained from modern NLP dependencyparsers. We apply our model extractor to four industrialrequirements documents, reporting on the frequency of differentextraction rules being applied. We conduct an expertstudy over one of these documents, investigating the accuracyand overall effectiveness of our domain model extractor.