Design of Test Data Generation Method for Dynamic- Functional Testing in Automatic Programming Assessment Using Flower Pollination Algorithm

Abstract

Automatic Programming Assessment (APA) is one of the vital methods that has been applied around Computer Science education in realizing automated marking and grading on students’ programming exercises or assignments. APA is fundamentally relying upon a test data generation process to perform a dynamic testing. Recently in Software Testing (ST) research, it has been proven that the adoption of any Meta-Heuristic Search Techniques (MHSTs) is able to improve the efficiency of generating adequate and optimal test data. Unfortunately, current studies on APA have not yet usefully incorporated the techniques to include a better quality program testing coverage by considering the optimal size of generated test data. Thus, our study propose a method of generating and locating an adequate test data with optimal in size by adapting a MHST to satisfy the dynamic-functional testing in APA (or is called DyFunFPA-TDG method). In this paper, we merely focus on revealing the design of the method with a sample of the generated test cases to be used in APA. This method able to assist educators of elementary programming courses to provide the means of deriving and generating adequate test data with optimal in size regardless of having the expertise in specific knowledge of test cases design.