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Dynamic Requirement Clustering of Requirement with Usable Test Cases by Cosine-Correlation

by Amit Verma, Chetna
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 11
Year of Publication: 2015
Authors: Amit Verma, Chetna
10.5120/ijca2015905594

Amit Verma, Chetna . Dynamic Requirement Clustering of Requirement with Usable Test Cases by Cosine-Correlation. International Journal of Computer Applications. 123, 11 ( August 2015), 22-24. DOI=10.5120/ijca2015905594

@article{ 10.5120/ijca2015905594,
author = { Amit Verma, Chetna },
title = { Dynamic Requirement Clustering of Requirement with Usable Test Cases by Cosine-Correlation },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 11 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number11/22007-2015905594/ },
doi = { 10.5120/ijca2015905594 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:10.838746+05:30
%A Amit Verma
%A Chetna
%T Dynamic Requirement Clustering of Requirement with Usable Test Cases by Cosine-Correlation
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 11
%P 22-24
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In software engineering testing plays an important role in development and maintenance of software. Component based software development gained a lot of practical importance in the field of Software engineering by the academic researcher and industry for finding reusable efficient test cases. It is the predominant problem in software engineering that clustering reduces the search space of the component of test cases by grouping of similar entities together ensuring reduce time complexity and reduce the search time for retrieve test cases according to requirement. In this research paper we investigate how k-mean work on the set of requirement and usable test cases we also define how to resolve the k-mean clustering static number of cluster when new requirement or test cases will come. In this research paper we investigate how k-mean work on the set of requirement and usable test cases we also define how to resolve the k-mean clustering static number of cluster when new requirement or test cases will come. Here we purposed an approach for dynamic clustering for test cases and requirement.

References
  1. Radhakrishna, Vangipuram, Chintakindi Srinivas, and CV Guru Rao. "Document Clustering Using Hybrid XOR Similarity Function for Efficient Software Component Reuse." Procedia Computer Science 17 (2013): 121-128.
  2. Milios, E., et al. "Automatic term extraction and document similarity in special text corpora." Proceedings of the sixth conference of the pacific association for computational linguistics. 2003.
  3. Panichella, Annibale, et al. "How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms." Proceedings of the 2013 International Conference on Software Engineering. IEEE Press, 2013
  4. Wang, James Z., and William Taylor. "Concept forest: A new ontology-assisted text document similarity measurement method." Web Intelligence, IEEE/WIC/ACM International Conference on. IEEE, 2007.
  5. Haiduc, Sonia, et al. "Automatic query reformulations for text retrieval in software engineering." Proceedings of the 2013 International Conference on Software Engineering. IEEE Press, 2013.
  6. Lee, M., Brandon Pincombe, and Matthew Welsh. "An empirical evaluation of models of text document similarity." Cognitive Science (2005).
  7. Haiduc, Sonia, et al. "Evaluating the specificity of text retrieval queries to support software engineering tasks." Software Engineering (ICSE), 2012 34th International Conference on. IEEE, 2012.
  8. Rao, Shivani, and Avinash Kak. "Retrieval from software libraries for bug localization: a comparative study of generic and composite text models."Proceedings of the 8th Working Conference on Mining Software Repositories. ACM, 2011.
  9. Bouras, Christos, and Vassilis Tsogkas. "A clustering technique for news articles using WordNet." Knowledge-Based Systems 36 (2012): 115-128.
  10. Leuski, Anton. "Evaluating document clustering for interactive information retrieval." Proceedings of the tenth international conference on Information and knowledge management. ACM, 2001.
  11. Bhatia, Sanjiv K., and Jitender S. Deogun. "Conceptual clustering in information retrieval." Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 28.3 (1998): 427-436.
  12. Tombros, Anastasios, Robert Villa, and Cornelis J. Van Rijsbergen. "The effectiveness of query-specific hierarchic clustering in information retrieval."Information processing & management 38.4 (2002): 559-582.
  13. Poshyvanyk, Denys, Malcom Gethers, and Andrian Marcus. "Concept location using formal concept analysis and information retrieval." ACM Transactions on Software Engineering and Methodology (TOSEM) 21.4 (2012): 23.
  14. Maitah, Wafa, Mamoun Al-Rababaa, and Ghasan Kannan. "Improving the Effectiveness of Information Retrieval System Using Adaptive Genetic Algorithm." International Journal of Computer Science & Information Technology 5.5 (2013): 91-105.
  15. Kettinger, William J., et al. "Strategic information systems revisited: a study in sustainability and performance." MIS quarterly (1994): 31-58.
  16. Dharmarajan, A., and T. Velmurugan. "Research Scholar, Research & Development Centre, Bharathiar University, Coimbatore-046, India."Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on. IEEE, 2013.
  17. Lew, Michael S., et al. "Content-based multimedia information retrieval: State of the art and challenges." ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 2.1 (2006): 1-19.
  18. Eriksen, Hallvard Andreas. "Requirements to ultrasound imaging workstations in a Chinese hospital.
Index Terms

Computer Science
Information Sciences

Keywords

Clustering correlation retrieval K-mean

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