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

Varun Jasuja; Rajesh Kumar Singh

doi:doi:10.11648/j.dmath.20170203.17

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

Varun Jasuja, Rajesh Kumar Singh

Published in International Journal of Discrete Mathematics (Volume 2, Issue 3)

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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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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TY  - JOUR
T1  - Effective Approach for Code Coverage Using Monte Carlo Techniques in Test Case Selection
AU  - Varun Jasuja
AU  - Rajesh Kumar Singh
Y1  - 2017/03/29
PY  - 2017
N1  - https://doi.org/10.11648/j.dmath.20170203.17
DO  - 10.11648/j.dmath.20170203.17
T2  - International Journal of Discrete Mathematics
JF  - International Journal of Discrete Mathematics
JO  - International Journal of Discrete Mathematics
SP  - 100
EP  - 106
PB  - Science Publishing Group
SN  - 2578-9252
UR  - https://doi.org/10.11648/j.dmath.20170203.17
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
IS  - 3
ER  -

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Author Information

Varun Jasuja

Computer Science and Engineering, Guru Nanak Institute of Technology, Ambala, India
Rajesh Kumar Singh

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

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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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
T1  - Effective Approach for Code Coverage Using Monte Carlo Techniques in Test Case Selection
AU  - Varun Jasuja
AU  - Rajesh Kumar Singh
Y1  - 2017/03/29
PY  - 2017
N1  - https://doi.org/10.11648/j.dmath.20170203.17
DO  - 10.11648/j.dmath.20170203.17
T2  - International Journal of Discrete Mathematics
JF  - International Journal of Discrete Mathematics
JO  - International Journal of Discrete Mathematics
SP  - 100
EP  - 106
PB  - Science Publishing Group
SN  - 2578-9252
UR  - https://doi.org/10.11648/j.dmath.20170203.17
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
IS  - 3
ER  -

Copy | Download