String test cases are required by many real-world applications to identify defects and security risks. Random Testing (RT) is a low cost and easy to implement testing approach to generate strings. However, its effectiveness is not satisfactory. In this research, black-box string test case generation methods are investigated. Two objective functions are introduced to produce effective test cases. The diversity of the test cases is the first objective, where it can be measured through string distance functions. The second objective is guiding the string length distribution into a Benford distribution based on the hypothesis that the population of strings is right-skewed within its range. When both objectives are applied via a multi-objective optimization algorithm, superior string test sets are produced. An empirical study is performed with several real-world programs indicating that the generated string test cases outperform test cases generated by other methods.

Although several distance or similarity functions for trees have been introduced, their performance is not always satisfactory in many applications, ranging from document clustering to natural language processing. This research proposes a new similarity function for trees, namely Extended Subtree (EST), where a new subtree mapping is proposed. EST generalizes the edit base distances by providing new rules for subtree mapping. Further, the new approach seeks to resolve the problems and limitations of previous approaches. Extensive evaluation frameworks are developed to evaluate the performance of the new approach against previous proposals. Clustering and classification case studies utilizing three real-world and one synthetic labeled data sets are performed to provide an unbiased evaluation where different distance functions are investigated. The experimental results demonstrate the superior performance of the proposed distance function. In addition, an empirical runtime analysis demonstrates that the new approach is one of the best tree distance functions in terms of runtime efficiency.

Although Random Testing (RT) is low cost and straightforward, its effectiveness is not satisfactory. To increase the effectiveness of RT, researchers have developed Adaptive Random Testing (ART) and Quasi-Random Testing (QRT) methods which attempt to maximize the test case coverage of the input domain. This paper proposes the use of Centroidal Voronoi Tessellations (CVT) to address this problem. Accordingly, a test case generation method, namely, Random Border CVT (RBCVT), is proposed which can enhance the previous RT methods to improve their coverage of the input space. The generated test cases by the other methods act as the input to the RBCVT algorithm and the output is an improved set of test cases. Therefore, RBCVT is not an independent method and is considered as an add-on to the previous methods. An extensive simulation study and a mutant-based software testing investigation have been performed to demonstrate the effectiveness of RBCVT against the ART and QRT methods. Results from the experimental frameworks demonstrate that RBCVT outperforms previous methods. In addition, a novel search algorithm has been incorporated into RBCVT reducing the order of computational complexity of the new approach. To further analyze the RBCVT method, randomness analysis was undertaken demonstrating that RBCVT has the same characteristics as ART methods in this regard.