IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

I-Way: A Cloud-Based Recommendation System for Software Requirement Reusability

I-Way: A Cloud-Based Recommendation System for Software Requirement Reusability
View Sample PDF
Author(s): Chetna Gupta (Jaypee Institute of Information Technology, Noida, India), Surbhi Singhal (Jaypee Institute of Information Technology, Noida, India) and Astha Kumari (Jaypee Institute of Information Technology, Noida, India)
Copyright: 2020
Pages: 12
Source title: Crowdsourcing and Probabilistic Decision-Making in Software Engineering: Emerging Research and Opportunities
Source Author(s)/Editor(s): Varun Gupta (University of Beira Interior, Covilha, Portugal)
DOI: 10.4018/978-1-5225-9659-2.ch002

Purchase

View I-Way: A Cloud-Based Recommendation System for Software Requirement Reusability on the publisher's website for pricing and purchasing information.

Abstract

This study addresses the problem of effectively searching and selecting relevant requirements for reuse meeting stakeholders' objectives through knowledge discovery and data mining techniques maintained over a cloud platform. Knowledge extraction of similar requirement(s) is performed on data and meta-data stored in central repository using a novel intersective way method (i-way), which uses intersection results of two machine learning algorithm namely, K-nearest neighbors (KNN) and term frequency-inverse document frequency (TF-IDF). I-way is a two-level extraction framework which represents win-win situation by considering intersective results of two different approaches to ensure that selection is progressing towards desired requirement for reuse consideration. The validity and effectiveness of results of proposed framework are evaluated on requirement dataset, which show that proposed approach can significantly help in reducing effort by selecting similar requirements of interest for reuse.

Related Content

Kamalendu Pal. © 2020. 22 pages.
Chetna Gupta, Surbhi Singhal, Astha Kumari. © 2020. 12 pages.
Sudha Srinivasan, D. S. Chauhan. © 2020. 16 pages.
Priyanka Chandani, Chetna Gupta. © 2020. 30 pages.
Chamundeswari Arumugam, Srinivasan Vaidyanathan. © 2020. 13 pages.
Varun Gupta, Aditya Raj Gupta, Utkarsh Agrawal, Ambika Kumar, Rahul Verma. © 2020. 15 pages.
Vimaladevi M., Zayaraz G.. © 2020. 25 pages.
Body Bottom