Model design for grammatical error identification in software requirements specification using statistics and rule-based techniques

Abstract

The statement of functional requirements statement that refers to software requirements specifications (SRS) is a reference for stakeholders in making software. The SRS document uses a different English grammar due to limited knowledge by the software development team, making it difficult for the development team to read and understand the specification documents for software requirements properly. To overcome this grammatical error, a method is needed to resolve grammatical errors. The method used to identify grammatical errors is based on a trigram language model. In the last research, trigram language models showed quite good performance in identifying grammatical errors based on probabilistic n-gram language models. To improve the performance of the n-gram language model this study also utilizes a corpus namely Penn Tree Bank Corpus. Provide recommendations, this study uses rules-based techniques. A number of rules are made to provide recommendations for inappropriate grammar. So that the software requirements specification compiler can check for grammatical errors and improve the quality of the software requirements specification document.