2023/02/14 by Jarrod Goschen, Goschen, Jarrod, Anna Sergeevna Bosman +3
Computer Science · #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Machine Learning and Data Classification #Neural and Evolutionary Computing (cs.NE) #Software Engineering (cs.SE) #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.2302.07646
openalex publication_date 2023/02/14 · openalex created_date 2023/02/18 · openalex updated_date 2026/07/28
Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the purpose of improving software engineering processes, particularly software testing. Existing state-of-the-art automated software testing techniques focus on utilising search algorithms to discover input values that achieve high execution path coverage. These algorithms are trained on the same code that they intend to test, requiring instrumentation and lengthy search times to test each software component. This paper outlines a novel genetic programming framework, where the evolved solutions are not input values, but micro-programs that can repeatedly generate input values to efficiently explore a software component's input parameter domain. We also argue that our approach can be generalised such as to be applied to many different software systems, and is thus not specific to merely the particular software component on which it was trained.