— Cause-effect graphing is a commonly used black-box technique with many applications in practice. It is important to be able to create accurate cause-effect graph specifications from system requirements before converting them to test case tables used for black-box testing. In this paper, a new graphical software tool for creating cause-effect graph specifications is presented. The tool uses standardized graphical notation for describing different types of nodes, logical relations and constraints, resulting in a visual representation of the desired cause-effect graph which can be exported for later usage and imported in the tool. The purpose of this work is to make the cause-effect graph specification process easier for users in order to solve some of the problems which arise due to the insufficient amount of understanding of cause-effect graph elements. The proposed tool was successfully used for creating cause-effect graph specifications for small, medium and large graphs. It was also successfully used for performing different types of tasks by users without any prior knowledge of the functionalities of the tool, indicating that the tool is easy to use, helpful and intuitive. The results indicate that the usage of standardized notation is easier to understand than non-standardized approaches from other tools.

Cause-effect graphs are a popular black-box testing technique. The most commonly used approach for generating test cases from cause-effect graph specifications uses backward-propagation of forced effect activations through the graph in order to get the values of causes for the desired test case. Many drawbacks have been identified when using this approach for different testing requirements. Several algorithms for automatically generating test case suites from cause-effect graph specifications have been proposed. However, many of these algorithms do not solve the main drawbacks of the initial back-propagation approach and offer only minor improvements for specific purposes. This work proposes two new algorithms for deriving test cases from cause-effect graph representations. Forward-propagation of cause values is used for generating the full feasible test case suite, whereas multiple effect activations are taken into account for reducing the feasible test case suite size. Evaluation of the test case suites generated by using the proposed algorithms was performed by using the newly introduced test effect coverage and fault detection rate effectiveness metrics. The evaluation shows that the proposed algorithms work in real time even for a very large number of cause nodes. The results also indicate that the proposed algorithm for generating all feasible test cases generates a larger test case suite, whereas the proposed algorithm for test case suite minimization generates a smaller test case subset than the originally proposed approaches while ensuring the maximum effect coverage, fault detection rate effectiveness and a better test effect coverage ratio.

Visual impairments often pose serious restrictions on a visually impaired person and there is a considerable number of persons, especially among aging population, which depend on assistive technology to sustain their quality of life. Development and testing of assistive technology for visually impaired requires gathering information and conducting studies on both healthy and visually impaired individuals in a controlled environment. We propose test setup for visually impaired persons by creating RFID based assistive environment – Visual Impairment Friendly RFID Room. The test setup can be used to evaluate RFID object localization and its use by visually impaired persons. To certain extent every impairment has individual characteristics as different individuals may better respond to different subsets of visual information. We use virtual reality prototype to both simulate visual impairment and map full visual information to the subset that visually impaired person can perceive. Time-domain color mapping real-time image processing is used to evaluate the virtual reality prototype targeting color vision deficiency.