Terminological inconsistency analysis of natural language requirements

Context: Terminological inconsistencies owing to errors in usage of terms in requirements specifications could result into subtle yet critical problems in interpreting and applying these specifications into various phases of SDLC.Objective: In this paper, we consider special class of terminological inconsistencies arising from term-aliasing, wherein multiple terms spread across a corpus of natural language text requirements may be referring to the same entity. Identification of such alias entity-terms is a difficult problem for manual analysis as well as for developing tool support.Method: We consider the case of syntactic as well as semantic aliasing and propose a systematic approach for identifying these. Identification of syntactic aliasing involves automated generation of patterns for identifying syntactic variances of terms including abbreviations and introduced-aliases. Identification of semantic aliasing involves extracting multidimensional features (linguistic, statistical, and locational) from given requirement text to estimate semantic relatedness among terms. Based upon the estimated relatedness and standard language database based refinement, clusters of potential semantic aliases are generated. Results of these analyses with user refinement lead to generation of entity-term alias glossary and unification of term usage across requirements.Results: A prototype tool was developed to assess the effectiveness of the proposed approach for an automated analysis of term-aliasing in the requirements given as plain English language text. Experimental results suggest that approach is effective in identifying syntactic as well as semantic aliases, however, when aiming for higher recall on larger corpus, user selection is necessary to eliminate false positives.Conclusion: This proposed approach reduces the time-consuming and error-prone task of identifying multiple terms which might be referring to the same entity to a process of tool assisted identification of such term-aliases.