To perform software testing at the early stages of software development process can save the cost and effort on finding and fixing defects. As the first stage of software development process, requirements engineering has been moved away from project-initiated requirements engineering towards requirements-initiated development in the last decade. This leads new challenges that it demands support for handling the requirements continually come in from multiple stakeholders on multiple abstraction levels instead of some specific customers. Requirements Abstraction Model was developed as a hierarchical abstraction method for requirements management, which is enable product management to leverage their resources and select requirements for implementation without overloading the organization. RAM was validated in industry on the usability for requirements management, but there is no evaluation for RAM on software testing. This thesis presents an empirical study with a goal of evaluating the suitability of RAM for test case design in respective of efficiency and effectiveness by the comparison with IEEE Std. 830 which is a standard of the traditional requirements specification. For achieving the goal of this study, a controlled experiment is conducted based on the refinement on an initial experiment planning, and is operated with twenty developers in industry in China. Analysis of the collected data from the experiment indicates that RAM has a similar effectiveness as using the requirements in IEEE Std. 830 format, while RAM is more efficient for test case design. Therefore, RAM is suitable for test case design, and has better performance than IEEE Std. 830 comprehensively in view of both efficiency and effectiveness.

Large and complex software that is frequently changed leads to testing challenges. It is well established that the later a fault is detected in software development, the more it costs to fix. This thesis aims to research and develop a method of generating relevant and non-redundant test cases for a regression test suite, to catch bugs as early in the development process as possible. The research was executed at Axis Communications AB with their products and systems in mind. The approach utilizes user data to dynamically generate a Markov chain model and with a Markov chain Monte Carlo method, strengthen that model. The model generates test case proposals, detects test gaps, and identifies redundant test cases based on the user data and data from a test suite. The sampling in the Markov chain Monte Carlo method can be modified to bias the model for test coverage or relevancy. The model is generated generically and can therefore be implemented in other API-driven systems. The model was designed with scalability in mind and further implementations can be made to increase the complexity and further specialize the model for individual needs.