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DOI: 10.14569/IJACSA.2016.070411
PDF

Regression Test-Selection Technique Using Component Model Based Modification: Code to Test Traceability

Author 1: Ahmad A. Saifan
Author 2: Mohammed Akour
Author 3: Iyad Alazzam
Author 4: Feras Hanandeh

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 4, 2016.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Regression testing is a safeguarding procedure to validate and verify adapted software, and guarantee that no errors have emerged. However, regression testing is very costly when testers need to re-execute all the test cases against the modified software. This paper proposes a new approach in regression test selection domain. The approach is based on meta-models (test models and structured models) to decrease the number of test cases to be used in the regression testing process. The approach has been evaluated using three Java applications. To measure the effectiveness of the proposed approach, we compare the results using the re-test to all approaches. The results have shown that our approach reduces the size of test suite without negative impact on the effectiveness of the fault detection.

Keywords: Regression Test; Regression Test selection technique; Meta-Model; Models Traceability

Ahmad A. Saifan, Mohammed Akour, Iyad Alazzam and Feras Hanandeh, “Regression Test-Selection Technique Using Component Model Based Modification: Code to Test Traceability” International Journal of Advanced Computer Science and Applications(IJACSA), 7(4), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070411

@article{Saifan2016,
title = {Regression Test-Selection Technique Using Component Model Based Modification: Code to Test Traceability},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070411},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070411},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {4},
author = {Ahmad A. Saifan and Mohammed Akour and Iyad Alazzam and Feras Hanandeh}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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