Actionable Analytics on Software Requirement Specifications
Lida Bamizadeh1, Binod Kumar2, Ajay Kumar3, Shailaja Shirwaikar4
1Lida Bamizadeh*, Department of Computer Science , Savitrabia Phule Pune University, Pune, India.
2Dr. Binod Kumar, Department of Computer Applications, JSPM Jayawant, Pune, India.
3Dr. Ajay Kumar, Technical Campus, JSPM Jayawant, Pune, India.
4Dr. Shailaja Shirwaikar, Department of Computer Science, Savitrabia Phule Pune University, Pune, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1921-1928 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5849018520/2020©BEIESP | DOI: 10.35940/ijrte.E5849.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The volume of data and need for churning this data to provide useful information has increased the scope of data mining and made it promising in recent years. Software intelligence (SI) (as the future of the mining software engineering data) presents theories and techniques to augment software decision making by using fact-based support systems. SI exposes software practitioners to up-to-date and relevant information to support their daily decision activities over the complete software development life cycle. Software documents contain important information for a plenty of software engineering tasks and one such important document is Software requirement specification (SRS) which details the system and user requirements. Inexplicit, ambiguous or imperfect requirements guide leads to a non-acceptable product by users. Constructing of a strong software specification can be supported by building a semantic space, validating new specification for completeness, categorization of software requirement specification and identification of significant concepts and related keywords. This paper proposes a knowledge management system for software document repositories using data analytics and demonstrates its creation and usage for a document set of software requirement specifications.
Keywords: Clustering, Semantic Analysis, Software Intelligence, Software Requirements Specification.
Scope of the Article: Clustering.