An ontology-based text processing approach for simplifying ambiguity of requirement specifications

In the last few years, several works in the literature of software engineering have addressed the problem of requirement management. A majority problem of software errors is introduced during the requirements phase because much of requirements specification is written in natural language format. As this, it is hard to identify consistencies because of too ambiguous for specification purpose. Therefore, this paper aims to propose a method for simplifying ambiguity of requirement specification documents through two concepts of ontology-based probabilistic text processing: Text classification and Text Filtering. Text classification is used to analyze and classify requirement specification having similar detail into the same class. This contributes to a better understanding of the impact of the requirements and to elaborate them. Meanwhile, text filters are used to leverage synopsis requirements in documents through probabilistic text classification technique. After testing by F-measure, the experimental results return a satisfactory accuracy. These demonstrate that our method may provide more effectiveness for simplifying ambiguity of requirement specifications.