Abstract:
Requirements are the functionalities that are discovered before building any
product. A systematic approach through which the software engineer collects
requirements from diverse sources and implements them into the software
development processes IS called Requirement engineering. Requirements
engineering contains a set of activities for discovering, analyzing, documenting,
validating and maintaining a set of requirements for a system. Functional
Requirements (FRs) and Non-Functional Requirements (NFRs) are two basic
types of requirements in Software Requirement Specification documents. The
classification of these requirements is an important task as it provides an ease for
the team manager and the software development first. The NFRs grabs less
attention from the development team. Some of the NFR categories are very
important to consider while developing the software. This research study proposes
a technique to classify the requirements in to FRs and NFRs with the help of
Machine Learning techniques. The NFRs are defined in the requirement
document but in some cases the NFRs are not clearly mentioned. Therefore, using
Machine Learning the requirements are classified and system will automatically
identify the categories of NFRss, which are evaluated using accuracy, precision
and F-Measure. The accuracy explains that whether the NFR is accurately
classified in the document, the precision explains whether the mentioned
requirements are properly placed in the classified field.F -measure or F-score is
the weighted average of precision and recall. Furthermore, it also classifies the
NFRs into sub categories. Different ML approaches and classification algorithms
will be used in the study.
