Manika Tyagi

Work place: Department of C.S.E., U.I.E.T. Kurukshetra University, Kurukshetra, India

E-mail: tmanika@gmail.com

Website:

Research Interests: Applied computer science, Computational Science and Engineering, Computer systems and computational processes, Theoretical Computer Science, Data Structures and Algorithms

Biography

Manika Tyagi is born in India. She is presently Assistant Professor in computer science and engineering department at Shobhit University, Meerut, India. She has got M.Tech degree from University Institute of Engineering and Technology, Kurukshetra University, kurukshetra, Haryana, India in 2014. Prior to it, she was lecturer in Vidya Bhawan College for Engineering and Technology, Kanpur, Uttar Pradesh, India. She has completed B.Tech in Computer Science and Engineering from Maharana Pratap Engineering College, Kanpur, Uttar Pradesh, India in 2011. Her research area includes regression testing, test case prioritization, test case selection, minimization.

Author Articles
An Approach for Test Case Prioritization Based on Three Factors

By Manika Tyagi Sona Malhotra

DOI: https://doi.org/10.5815/ijitcs.2015.04.09, Pub. Date: 8 Mar. 2015

The main aim of regression testing is to test the modified software during maintenance level. It is an expensive activity, and it assures that modifications performed in software are correct. An easiest strategy to regression testing is to re-test all test cases in a test suite, but due to limitation of resource and time, it is inefficient to implement. Therefore, it is necessary to discover the techniques with the goal of increasing the regression testing’s effectiveness, by arranging test cases of test suites according to some objective criteria. Test case prioritization intends to arrange test cases in such a manner that higher priority test cases execute earlier than test cases of lower priority according to some performance criteria. This paper presents an approach to prioritize regression test cases based on three factors which are rate of fault detection [6], percentage of fault detected and risk detection ability. The proposed approach is compared with different prioritization techniques such as no prioritization, reverse prioritization, random prioritization, and also with previous work of kavitha et al [6], using APFD (average percentage of fault detected) metric. The results represent that proposed approach outperformed all approaches mentioned above.

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