Automated Software Testing Analysis on Test Case Optimization and Test Case Levels on Java and Python
Keywords:
Automated Software Testing, Test Case Optimization, Object Oriented Programming, Integrated Development Environment. Logical Flaws, Inherent Complexity, Syntax Issues.Abstract
In today's world, object-oriented programming (OOP) languages are commonly employed in software development. The architecture and design of the Integrated Development Environment (IDE) models is constantly updated and modified to facilitate programming practice, keeping in mind the popularity of OOP languages. However, a huge percentage of syntactic and logical errors are caused by complicated programming structures and the inherent complexity of underlying applications. Although syntax issues are easier to identify during compilation, the majority of IDEs fail to detect logical flaws, resulting in not just debugging costs but also milestone delays. The goal of this study is to provide a new design model for IDEs that will allow them to detect logical mistakes. This will make it easier for developers to spot logical problems sooner in the development process, resulting in reduced development time and on-time milestone delivery. The proposed model will be implemented as a working Python IDE application and compared to other current state-of-the-art products on the market. The amount of logical mistakes found will be used to assess the proposed model's correctness. Python was chosen because of its broad application in Artificial Intelligence and Data Science. Machine learning approaches have been used to the process of student modelling, particularly in the development of tutors to help students learn to programme. These were created for a variety of languages (Pascal, Prolog, Lisp, C++), as well as programming paradigms (procedural and declarative), but never for Java object-oriented programming. Using inconsistencies between a student and the right software, JavaBugs automatically creates a bug library. While other studies look at code snippets or UML diagrams to infer student knowledge of object-oriented design and programming, JavaBugs looks at a complete Java programme and finds the most similar correct programme to the student's solution among a collection of correct solutions, then uses similarity measures and background knowledge to build trees of misconceptions. Experiments demonstrate that JavaBugs can find the most comparable correct programme 97 percent of the time, as well as discover and detect 61.4 percent of expert-identified student mistakes.
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