Cite Article

Automated Test Cases and Test Data Generation for Dynamic Structural Testing in Automatic Programming Assessment Using MC/DC

Choose citation format

BibTeX

@article{IJASEIT10166,
   author = {Rohaida Romli and Shahadath Sarker and Mazni Omar and Musyrifah Mahmod},
   title = {Automated Test Cases and Test Data Generation for Dynamic Structural Testing in Automatic Programming Assessment Using MC/DC},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {1},
   year = {2020},
   pages = {120--127},
   keywords = {automatic programming assessment; test data generation; dynamic testing; structural coverage; MC/DC.},
   abstract = {Automatic Programming Assessment (or APA) is known as a method to assist educators in executing automated assessment and grading on students’ programming exercises and assignments. Having to execute dynamic testing in APA, providing an adequate set of test data via a systematic process of test data generation is necessarily essential. Though researches respecting to software testing have proposed various significant methods to realize automated test data generation, it occurs that recent studies of APA rarely utilized these methods. Merely some of the limited studies appeared to resolve this circumstance, yet the focus on realizing test set and test data covering more thorough dynamic-structural testing are still deficient. Thus, we propose a method that utilizes MC/DC coverage criteria to support more thorough automated test data generation for dynamic-structural testing in APA (or is called DyStruc-TDG). In this paper, we reveal the means of deriving and generating test cases and test data for the DyStruc-TDG method and its verification concerning the reliability criteria (or called positive testing) of test data adequacy in programming assessments. This method offers a significant impact on assisting educators dealing with introductory programming courses to derive and generate test cases and test data via APA regardless of having knowledge of designing test cases mainly to execute structural testing. As regards to this, it can effectively reduce the educators’ workload as the process of manual assessments is typically prone to errors and promoting inconsistency in marking and grading.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10166},
   doi = {10.18517/ijaseit.10.1.10166}
}

EndNote

%A Romli, Rohaida
%A Sarker, Shahadath
%A Omar, Mazni
%A Mahmod, Musyrifah
%D 2020
%T Automated Test Cases and Test Data Generation for Dynamic Structural Testing in Automatic Programming Assessment Using MC/DC
%B 2020
%9 automatic programming assessment; test data generation; dynamic testing; structural coverage; MC/DC.
%! Automated Test Cases and Test Data Generation for Dynamic Structural Testing in Automatic Programming Assessment Using MC/DC
%K automatic programming assessment; test data generation; dynamic testing; structural coverage; MC/DC.
%X Automatic Programming Assessment (or APA) is known as a method to assist educators in executing automated assessment and grading on students’ programming exercises and assignments. Having to execute dynamic testing in APA, providing an adequate set of test data via a systematic process of test data generation is necessarily essential. Though researches respecting to software testing have proposed various significant methods to realize automated test data generation, it occurs that recent studies of APA rarely utilized these methods. Merely some of the limited studies appeared to resolve this circumstance, yet the focus on realizing test set and test data covering more thorough dynamic-structural testing are still deficient. Thus, we propose a method that utilizes MC/DC coverage criteria to support more thorough automated test data generation for dynamic-structural testing in APA (or is called DyStruc-TDG). In this paper, we reveal the means of deriving and generating test cases and test data for the DyStruc-TDG method and its verification concerning the reliability criteria (or called positive testing) of test data adequacy in programming assessments. This method offers a significant impact on assisting educators dealing with introductory programming courses to derive and generate test cases and test data via APA regardless of having knowledge of designing test cases mainly to execute structural testing. As regards to this, it can effectively reduce the educators’ workload as the process of manual assessments is typically prone to errors and promoting inconsistency in marking and grading.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10166
%R doi:10.18517/ijaseit.10.1.10166
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 1
%@ 2088-5334

IEEE

Rohaida Romli,Shahadath Sarker,Mazni Omar and Musyrifah Mahmod,"Automated Test Cases and Test Data Generation for Dynamic Structural Testing in Automatic Programming Assessment Using MC/DC," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 1, pp. 120-127, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.1.10166.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Romli, Rohaida
AU  - Sarker, Shahadath
AU  - Omar, Mazni
AU  - Mahmod, Musyrifah
PY  - 2020
TI  - Automated Test Cases and Test Data Generation for Dynamic Structural Testing in Automatic Programming Assessment Using MC/DC
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 1
Y2  - 2020
SP  - 120
EP  - 127
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - automatic programming assessment; test data generation; dynamic testing; structural coverage; MC/DC.
N2  - Automatic Programming Assessment (or APA) is known as a method to assist educators in executing automated assessment and grading on students’ programming exercises and assignments. Having to execute dynamic testing in APA, providing an adequate set of test data via a systematic process of test data generation is necessarily essential. Though researches respecting to software testing have proposed various significant methods to realize automated test data generation, it occurs that recent studies of APA rarely utilized these methods. Merely some of the limited studies appeared to resolve this circumstance, yet the focus on realizing test set and test data covering more thorough dynamic-structural testing are still deficient. Thus, we propose a method that utilizes MC/DC coverage criteria to support more thorough automated test data generation for dynamic-structural testing in APA (or is called DyStruc-TDG). In this paper, we reveal the means of deriving and generating test cases and test data for the DyStruc-TDG method and its verification concerning the reliability criteria (or called positive testing) of test data adequacy in programming assessments. This method offers a significant impact on assisting educators dealing with introductory programming courses to derive and generate test cases and test data via APA regardless of having knowledge of designing test cases mainly to execute structural testing. As regards to this, it can effectively reduce the educators’ workload as the process of manual assessments is typically prone to errors and promoting inconsistency in marking and grading.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10166
DO  - 10.18517/ijaseit.10.1.10166

RefWorks

RT Journal Article
ID 10166
A1 Romli, Rohaida
A1 Sarker, Shahadath
A1 Omar, Mazni
A1 Mahmod, Musyrifah
T1 Automated Test Cases and Test Data Generation for Dynamic Structural Testing in Automatic Programming Assessment Using MC/DC
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 1
YR 2020
SP 120
OP 127
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 automatic programming assessment; test data generation; dynamic testing; structural coverage; MC/DC.
AB Automatic Programming Assessment (or APA) is known as a method to assist educators in executing automated assessment and grading on students’ programming exercises and assignments. Having to execute dynamic testing in APA, providing an adequate set of test data via a systematic process of test data generation is necessarily essential. Though researches respecting to software testing have proposed various significant methods to realize automated test data generation, it occurs that recent studies of APA rarely utilized these methods. Merely some of the limited studies appeared to resolve this circumstance, yet the focus on realizing test set and test data covering more thorough dynamic-structural testing are still deficient. Thus, we propose a method that utilizes MC/DC coverage criteria to support more thorough automated test data generation for dynamic-structural testing in APA (or is called DyStruc-TDG). In this paper, we reveal the means of deriving and generating test cases and test data for the DyStruc-TDG method and its verification concerning the reliability criteria (or called positive testing) of test data adequacy in programming assessments. This method offers a significant impact on assisting educators dealing with introductory programming courses to derive and generate test cases and test data via APA regardless of having knowledge of designing test cases mainly to execute structural testing. As regards to this, it can effectively reduce the educators’ workload as the process of manual assessments is typically prone to errors and promoting inconsistency in marking and grading.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10166
DO  - 10.18517/ijaseit.10.1.10166