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Effective Approach for Code Coverage Using Monte Carlo Techniques in Test Case Selection

Received: 20 February 2017     Accepted: 13 March 2017     Published: 29 March 2017
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Abstract

Source code analysis alludes to the profound examination of source code and/or gathered form of code with a specific end goal to help discover the imperfections as far as security, comprehensibility, understanding and related parameters. In a perfect world, such systems consequently discover the defects with such a high level of certainty that what's found is surely a blemish. Notwithstanding, this is past the best in class for some sorts of utilization security defects. In this manner, such devices much of the time serve as helps for an examiner to help them focus in on security pertinent segments of code so they can discover blemishes all the more productively, instead of a device that just consequently discovers imperfections. Code Coverage is a measure used to portray the extent to which the source code of a system is tried by a specific test suite. A project with high code scope has been all the more completely tried and has a lower shot of containing software bugs than a system with low code scope. A wide range of measurements can be utilized to ascertain code scope; the absolute most fundamental are the percent of system subroutines and the percent of project articulations called amid execution of the test suite. This research work focus on the quality of source code using code coverage and analysis techniques. In the proposed research work, an effective model based approach shall be developed and implemented to improve the performance of code in terms of overall code coverage time, code complexity and related metrics.

Published in International Journal of Discrete Mathematics (Volume 2, Issue 3)
DOI 10.11648/j.dmath.20170203.17
Page(s) 100-106
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Code Coverage, Software Testing, Automated Test Case Generation

References
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[5] B. Boehm, “Cost Models for Future Software Life Cycle Processes: COCOMO 2.0”, U.S. Center for Software Engineering, Amsterdam, 1995, pp. 57-94.
[6] N. E. Fenton, M. Neil, “Software Metrics: Roadmap”, International Conference on Software Engineering, Limerick - Ireland, 2000, pp. 357–370.
[7] M. K. Daskalantonakis, “A Pratical View of Software Measurement and Implementation Experiences Within Motorola”, IEEE Transactions on Software Engineering, vol 18, 1992, pp. 998–1010.
[8] R. S. Pressman, "Software engineering a practitioner's approach", 4th. ed, McGraw-Hill, New York - USA, 1997, pp. 852.
[9] I. Sommerville, “Engenharia de Software”, Addison-Wesley, 6° Edição, São Paulo – SP, 2004.
[10] D. C. Ince, M. J. Sheppard, "System design metrics: a review and perspective", Second IEE/BCS Conference, Liverpool - UK, 1988, pp. 23-27.
[11] L. C. Briand, S. Morasca, V. R. Basili, “An Operational Process for Goal-Driven Definition of Measures”, Software Engineering - IEEE Transactions, vol 28, 2002, pp. 1106-1125.
[12] Refactorit tool, online, last update: 01/2008, available: http://www.aqris.com/display/ap/RefactorIt.
[13] O. Burn, CheckStyle, online, last update: 12/2007, available: http://eclipse-cs.sourceforge.net/index.shtml.
[14] M. G. Bocco, M. Piattini, C. Calero, "A Survey of Metrics for UML Class Diagrams", Journal of Object Technology 4, 2005, pp. 59-92.
[15] J Depend tool, online, last update: 03/2006, available: http://www.clarkware.com/software/JDepend.html.
[16] Metrics Eclipse Plugin, online, last update: 07/2005, available: http://sourceforge.net/projects/metrics.
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Cite This Article
  • APA Style

    Varun Jasuja, Rajesh Kumar Singh. (2017). Effective Approach for Code Coverage Using Monte Carlo Techniques in Test Case Selection. International Journal of Discrete Mathematics, 2(3), 100-106. https://doi.org/10.11648/j.dmath.20170203.17

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    ACS Style

    Varun Jasuja; Rajesh Kumar Singh. Effective Approach for Code Coverage Using Monte Carlo Techniques in Test Case Selection. Int. J. Discrete Math. 2017, 2(3), 100-106. doi: 10.11648/j.dmath.20170203.17

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    AMA Style

    Varun Jasuja, Rajesh Kumar Singh. Effective Approach for Code Coverage Using Monte Carlo Techniques in Test Case Selection. Int J Discrete Math. 2017;2(3):100-106. doi: 10.11648/j.dmath.20170203.17

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  • @article{10.11648/j.dmath.20170203.17,
      author = {Varun Jasuja and Rajesh Kumar Singh},
      title = {Effective Approach for Code Coverage Using Monte Carlo Techniques in Test Case Selection},
      journal = {International Journal of Discrete Mathematics},
      volume = {2},
      number = {3},
      pages = {100-106},
      doi = {10.11648/j.dmath.20170203.17},
      url = {https://doi.org/10.11648/j.dmath.20170203.17},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.dmath.20170203.17},
      abstract = {Source code analysis alludes to the profound examination of source code and/or gathered form of code with a specific end goal to help discover the imperfections as far as security, comprehensibility, understanding and related parameters. In a perfect world, such systems consequently discover the defects with such a high level of certainty that what's found is surely a blemish. Notwithstanding, this is past the best in class for some sorts of utilization security defects. In this manner, such devices much of the time serve as helps for an examiner to help them focus in on security pertinent segments of code so they can discover blemishes all the more productively, instead of a device that just consequently discovers imperfections. Code Coverage is a measure used to portray the extent to which the source code of a system is tried by a specific test suite. A project with high code scope has been all the more completely tried and has a lower shot of containing software bugs than a system with low code scope. A wide range of measurements can be utilized to ascertain code scope; the absolute most fundamental are the percent of system subroutines and the percent of project articulations called amid execution of the test suite. This research work focus on the quality of source code using code coverage and analysis techniques. In the proposed research work, an effective model based approach shall be developed and implemented to improve the performance of code in terms of overall code coverage time, code complexity and related metrics.},
     year = {2017}
    }
    

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    JF  - International Journal of Discrete Mathematics
    JO  - International Journal of Discrete Mathematics
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    EP  - 106
    PB  - Science Publishing Group
    SN  - 2578-9252
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    AB  - Source code analysis alludes to the profound examination of source code and/or gathered form of code with a specific end goal to help discover the imperfections as far as security, comprehensibility, understanding and related parameters. In a perfect world, such systems consequently discover the defects with such a high level of certainty that what's found is surely a blemish. Notwithstanding, this is past the best in class for some sorts of utilization security defects. In this manner, such devices much of the time serve as helps for an examiner to help them focus in on security pertinent segments of code so they can discover blemishes all the more productively, instead of a device that just consequently discovers imperfections. Code Coverage is a measure used to portray the extent to which the source code of a system is tried by a specific test suite. A project with high code scope has been all the more completely tried and has a lower shot of containing software bugs than a system with low code scope. A wide range of measurements can be utilized to ascertain code scope; the absolute most fundamental are the percent of system subroutines and the percent of project articulations called amid execution of the test suite. This research work focus on the quality of source code using code coverage and analysis techniques. In the proposed research work, an effective model based approach shall be developed and implemented to improve the performance of code in terms of overall code coverage time, code complexity and related metrics.
    VL  - 2
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Author Information
  • Computer Science and Engineering, Guru Nanak Institute of Technology, Ambala, India

  • Computer Science Application, SUS Institute of Computer, Tangori, India

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