Automated Test Cases and Test Data Generation for Dynamic Structural Testing in Automatic Programming Assessment Using 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.

Test Case Quality Factors

“…However, it is very difficult. In designing test cases, it is notable for ensuring that testing could achieve a certain level of thoroughness [22]. Hence, software testers must have sufficient skills to write good test cases [21], [23], [24], which really requires them to have a transparent knowledge on the system being tested [25].…”

MT-Forward Algorithm for Prediction of Project Categories Based on Selection of Mutants

“…The quality of a software product is affected by the quality of software testing [1]- [3]. Several of the techniques used is the approach of making the test case [4] and automatic assessment [5], [6]. One of them is mutation testing.…”