Test case design and generation is a tedious manual process that requires 40-70% of the software test life cycle. The test cases written manually by inexperienced testers may not offer a complete coverage of the requirements. Frequent changes in requirements reduce the reusability of the manually written test cases costing more time and effort. Most projects in the industry follow a Behavior-Driven software development approach to capturing requirements from the business stakeholders through user stories written in natural language. Instead of writing test cases manually, this thesis investigates a practical solution for automatically generating test cases within an Agile software development workflow using natural language-based user stories and acceptance criteria. However, the information provided by the user story is insufficient to create test cases using natural language processing (NLP), so we have introduced two new input parameters, Test Scenario Description and Dictionary, to improve the test case generation process. To establish the feasibility, we developed a tool that uses NLP techniques to generate functional test cases from the free-form test scenario description automatically. The tool reduces the effort required to create the test cases while improving the test coverage and quality of the test suite. Results from the feasibility study are presented in this thesis. / Master of Science

Software testing is a very important phase in software development as it minimize risks ina software system, however, it consumes time and can be very expensive. With automatictest case generation time consumption and cost can be reduced. Model-based testing isa method to test a software system with a model of the systems behaviour. Automatictest case generation is often considered a favorable support in model-based testing. In thiswork, the concept of model-based testing is explored along with testing the embedded partof a water-level measurement system (WLM) to investigate the efficiency of model-basedtesting on a software system. As a result of this, a model-based testing tool, MoMut::UMLis used to generate the test-cases on the UML model of WLM system that is built ina UML modeling environment, Eclipse-Papyrus. However, MoMut::UML implements aspecial type of model-based testing technique, model-based mutation testing; that injectsfaults in the UML model, and generates test-data on the fault-based model. By this, thebehaviour of system-under-test, only the UML model of water-level measurement system,is tested.

Unit testing plays a crucial role in ensuring the quality and reliability of software systems. However, manual testing can often be a slow and time-consuming process. With current advancements in artificial intelligence (AI), new tools have emerged for automated unit testing to address this issue. But how do these new AI tools compare to conventional automated unit test generation tools? To answer this question, we compared two state-of-the-art conventional unit test tools (EVOSUITE and RANDOOP) with the sole commercially available AI-based unit test tool (DIFFBLUE COVER) for Java. We tested them on 10 sample classes from 3 real-life projects provided by the Defects4J dataset to evaluate their performance regarding code coverage, mutation score, and fault detection. The results showed that EVOSUITE achieved the highest code coverage, averaging 89%, while RANDOOP and DIFFBLUE COVER achieved similar results, averaging 63%. In terms of mutation score, DIFFBLUE COVER had the lowest average score of 40%, while EVOSUITE and RANDOOP scored 67% and 50%, respectively. For fault detection, EVOSUITE and RANDOOP detected a higher number of bugs (7 out of 10 and 5 out of 10, respectively) compared to DIFFBLUE COVER, which found only 4 out of 10. Although the AI-based tool was outperformed in all three criteria, it still shows promise by being able to achieve adequate results, in some cases even surpassing the conventional tools while generating a significantly smaller number of total assertions and more comprehensive tests. Nonetheless, the study acknowledges its limitations in terms of the restricted number of AI-based tools used and the small number of projects utilized from Defects4J.