As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
This paper aims at demonstrating non-functional requirements analysis requirements analysis normally used supervised methods which need a lot of manual annotation work. Using unsupervised approaches to classify non-functional requirements can save a lot of labor and time, but the accuracy of the existing approaches is relatively low. In order to solve the dilemma, we propose a new clustering approach in this paper. The approach is an improved version of the previous aspect segmentation approach, but differs in terms of classification strategy, the representation of the review sentences, and the strategy for selecting new keywords. Experiments are conducted and compared on a software reviews dataset. Results show an improved performance of the new approach.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.