Traditional software testing is a labor-intensive and expensive manual process. To mitigate the high cost of manual test case generation, researchers have developed various techniques for automated test case generation over the last few decades. These techniques make use of static type information to determine which data types should be used in new test cases. Dynamically typed languages like JavaScript do not provide type information. The lack of type information poses a new challenge for test case generation techniques.
In this thesis, we propose a novel unsupervised probabilistic type inference approach to infer data types in a test case generation context. The approach uses both static and dynamic type inference techniques. We implemented the approach in a novel tool called SYNTEST-JAVASCRIPT which is an extension of the SYNTEST-FRAMEWORK. We evaluate the performance of the approach compared to random type sampling with respect to branch coverage. The evaluation is done using a custom benchmark of 97 units under test.
Our results show that using statically inferred type achieves a statistically significant increase in 54% of the benchmark files compared to the baseline. The combination of using both statically and dynamically inferred types improves the approach slightly with a significant increase in 56% of the benchmark files compared to the baseline. Finally, the results show that the time consumed by the static and dynamic type inference is insignificant compared to the total time budget and is worthwhile given the performance boost type inference provides.

Randomized variational operators can be very disruptive to the search process, especially when there exist dependencies between the variables under search. Within test-cases, these dependencies exist as well. This makes it interesting to evaluate the benefits of preserving these dependencies during test-case generation.

In this paper, we propose two variants of the Many-Objective Sorting Algorithm (MOSA). The first of which is based on Agglomerative Clustering, ACMOSA. The second is a Gene-pool Optimal Mixing based variant, GOMOSA. ACMOSA and GOMOSA model the inter-gene dependencies and use that model to intelligently perform crossover while preserving key building blocks within individuals. These novel techniques are evaluated in an empirical study and compared to MOSA and the Many Independent Objective algorithm (MIO). This study is composed of several benchmark RESTful APIs for which the algorithms generate test-cases.

The results of the empirical study show that, for 40% of the tested APIs, the novel techniques provide a significant benefit time-wise. For another 40% of the APIs, they perform equally well, and for 20% of the APIs under evaluation they performed worse.