Using Aggloramative Clustering to Asses and Improve Software Reliability
Venkateswar Rao1, Pravallika2, Manogna3, Sree Ram4
1P.Venkateswara rao, Asst. Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education and Foundation, Guntur, India.
2Pravallika, Department of Computer Science and Engineering, Koneru Lakshmaiah Education and Foundation, Guntur, India.
3Manogna, Department of Computer Science and Engineering, Koneru Lakshmaiah Education and Foundation, Guntur, India.
4SreeRam,Department of Computer Science and Engineering, Koneru Lakshmaiah Education and Foundation, Guntur, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 10530-10535 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4543118419/2019©BEIESP | DOI: 10.35940/ijrte.D4543.118419
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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: For the reduction of cost in software testing we propose a novel technique for testing and classifying methods based on clustering methods for classifying test cases for powerful and non-viable groups. This technique is based on data treatment obtained by pre-release of program while testing. Here we introduce 2 new clustering algorithms such as centroid and hierarchical based clustering. The test study expresses that the experiment bunching results can be distinguished viably with high review proportion and noteworthy rate exactness. The present paper tells about the presentation of clustering which move towards by comparing and investigating the factors like criteria coverage, features of construction and pre-release faults quality.
Keywords: Clustering Algorithms, Pre-Release Faults, Data Mining.
Scope of the Article: Data Mining.