Test data generation method for dynamic - structural testing in automatic programming assessment

Automatic Programming Assessment or so-called APA has being known as a significant method
in assisting lecturers to perform automated assessment and grading on students’ programming
assignments. Having to execute a dynamic testing in APA, it is necessary to prepare a set of test
data through a systematic test data generation process. Particularly focusing on the software
testing research area, various automated methods for test data generation have been proposed.
However, they are rarely being utilized in recent studies of APA. There have been limited early
attempts to integrate APA and test data generation, but there is still a lack of research in deriving
and generating test data for dynamic structural testing. To bridge the gap this study proposes a
method of test data generation for dynamic structural testing (or is called DyStruc-TDG).
DyStruc-TDG is realized as a tangible deliverable that acts as a test data generator to support
APA. The findings from conducted controlled experiment that is based on one-group pre-test and
post-test design depict that DyStruc-TDG improves the criteria of reliability (or called positive
testing) of test data adequacy in programming assessments. The proposed method is expectantly
to assist the lecturers who teach introductory programming courses to derive and generate test
data and test cases to perform automatic programming assessment regardless of having a
particular knowledge of test cases design in conducting a structural testing. By utilizing this
method as part of APA, the lecturers’ workload can be reduced effectively since the typical
manual assessments are always prone to errors and leading to inconsistency.