Software testing is an important but time-consuming task, making automatic test case generation an appealing solution. The current state-of-the-art algorithm for test case generation is DynaMOSA, which is an improvement of NSGA-II that applies domain knowledge to make it more suitable for test case generation. Although these enhancements are applicable to other evolutionary algorithms,
no research has been done on how effective other algorithms can function as the base. In this paper, we apply the DynaMOSA modifications to SPEA-II to create a new algorithm, DynaSPEA-II. We conduct an empirical experiment where we evaluate the DynaMOSA enhancements, and directly compare DynaSPEA-II to
DynaMOSA. The algorithms are assessed on a benchmark consisting of 36 diverse JavaScript classes w.r.t. branch coverage. Our results show that adding DynaMOSA enhancements to SPEA-II results in higher coverage in 13.9% of classes, with an average increase of 4.92% for classes where a statistically significant difference was found. DynaSPEA-II performed equally to DynaMOSA, with no statistically significant difference being found between the two.

In recent decades, automatic test generation has advanced significantly, providing developers with time-saving benefits and facilitating software debugging. While most research in this field focused on search-based test generation tools for statically-typed languages, only a few have been adapted for dynamically-typed languages. The larger search-space, generated by the dynamic allocation of types, causes standard search-based algorithms to not be as efficient in this domain and requiring a different approach. Existing algorithms like NSGA-II, MOSA and DynaMOSA have been employed to address this problem, but exploring different approaches may yield better results. That is why this paper proposes a different procedure based on an adaptation of the particle swarm optimization algorithm (PSO). The adaptation was evaluated using
the SynTest framework, showing that DynaMOSA achieves better results than the presented approach, both when comparing the PSO adaptation with and without DynaMOSA features, and when comparing the base DynaMOSA algorithm with PSO adapted to include DynaMOSA ingredients.

Software testing is an important yet time consuming task in the software development life cycle. Artificial Intelligence (AI) algorithms have been used to automate this task and have proven to be proficient at it. This research focuses on the automated testing of JavaScript programs, and builds upon the existing SynTest framework that is the current state of the art, with the Dynamic Many Objective Sorting Algorithm (DynaMOSA) being the best performing AI algorithm for test case generation. DynaMOSA uses the Non-Dominated Sorting Algorithm - II (NSGA-II) as its base algorithm, and adds modifications to it. This paper investigates whether the use of the Pareto Envelope Based Search Algorithm - II (PESA-II) as the base algorithm results in improved performance. The contributions of this research includes a modified PESA-II integrated into the SynTest framework, using inspiration from DynaMOSA. Moreover, we answer the question "How does the modified PESA- II perform compared to DynaMOSA in generating test cases for JavaScript programs?" The performance of the algorithms is measured based on the (branch and method) coverage of the test cases generated for a suite of JavaScript classes. The results show that the modified version of PESA-II outperforms the base version. However, neither manage to outperform DynaMOSA.

Software testing is a laborious job, and accounts for a large portion of software development expenses. Search-based automatic test case generation is an area of research that attempts to remedy this by discovering algorithms suited for generating test cases automatically. In this field, DynaMOSA is a state-of-the-art evolutionary algorithm, which reduces the problem of test case generation to a multi-objective optimization problem, and uses domain knowledge to generate solutions efficiently. In this paper, we adapt Pareto Corner Search Evolutionary Algorithm (PCSEA) for test case generation. Furthermore, we integrate DynaMOSA heuristics into PCSEA, and create a novel algorithm, DynaMOSAPCSEA. We evaluate the test case generation efficacy of PCSEA, DynaMOSA, and DynaMOSAPCSEA by using the JavaScript benchmark provided by SynTest Framework. The results indicate that PCSEA is a feasible algorithm for test case generation, however DynaMOSA heuristics improve its performance only minimally.

On the intuitive level, software testing is important because it assures the quality of the software used by humans. However, ensuring this quality is not an easy task because as the complexity of the software increases, so do the efforts to test it. Search-based software testing is an active research field that develops and explores tools for automatic test case generation. Their work involves using meta-heuristic search optimisation approaches such as evolutionary algorithms
from the evolutionary computation community and applying them to test case generation. The crossover between the evolutionary computation domain and the test case generation one produced DynaMOSA, a state-of-the-art evolutionary algorithm for generating test cases. In an attempt to
produce another well-performing algorithm, this paper performs another crossover between the two communities and creates DynaMOSARVEA - a product of a Reference Vector Guided Evolutionary Algorithm (RVEA) and DynaMOSA.
The conducted empirical study showed that although DynaMOSARVEA does not outperform DynaMOSA, it did outperform RVEA, thus demonstrating the value brought by domain-specific knowledge.