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

Rohaida Romli, Shahadath Sarker, Mazni Omar, Musyrifah Mahmod


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.


automatic programming assessment; test data generation; dynamic testing; structural coverage; MC/DC.

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