A Review of Test Case Prioritization using a novel Density based K-means Clustering

  Ishadeep Kaur Luthra,  Harsimranjit Kaur

Regression testing is used to validate the modified software, but unfortunately regression testing is very time consuming and cost inefficient as engineers has to perform the whole test again and again. In this case prioritization technique is used in regression testing. Different methodologies has been used till date for prioritization regression testing such as Genetic algorithm, Greedy search algorithm, particle swarm optimizer and many more. Many new method has also been used along with prioritization is clustering approach. It divides the test cases according to their properties and form different clusters. Prioritization is done on the basis of clusters formed. Hence, this paper reviews the different methods used in prioritization regression testing and what can be used next to obtain results that are cost and time efficient and give maximum result

Keywords- Component, formatting, style, styling, insert

Unique Identification Number - IJEDR1503079
Page Number(s) -
Pubished in - Volume 3 | Issue 3 | 28 August 2015
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Ishadeep Kaur Luthra,  Harsimranjit Kaur,   "A Review of Test Case Prioritization using a novel Density based K-means Clustering", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.3, Issue 3, pp., 28 August 2015, Available at :http://www.ijedr.org/papers/IJEDR1503079.pdf