Search-based test generation commonly uses fitness functions based on branch distances, i.e., estimations of how close conditional statements in a program are to evaluating to true or to false. When conditional statements depend on Boolean variables or Boolean-valued methods, the branch distance metric is unable to provide any guidance to the search, causing challenging plateaus in the fitness landscape. A commonly proposed solution is to apply testability transformations, which transform the program in a way that avoids conditional statements from depending on Boolean values. In this paper we introduce the concept of Certainty Booleans, which encode how certain a true or false Boolean value is. Using these Certainty Booleans, a basic testability transformation allows to restore gradients in the fitness landscape for Boolean branches, even when Boolean values are the result of complex interprocedural calculations. Evaluation on a set of complex Java classes and the EvoSuite test generator shows that this testability transformation substantially alters the fitness landscape for Boolean branches, and the altered fitness landscape leads to performance improvements. However, Boolean branches turn out to be much rarer than anticipated, such that the overall effects on code coverage are minimal.

Concurrent model synchronisation, i.e. the (bidirectional) propagation of updates between two models, is an important problem in the area of model-driven engineering (MDE). Compared to other consistency management tasks, synchronising concurrent updates is especially challenging as they can be conflicting, such that restoring a consistent state is not possible when all updates must be considered. Recent approaches create a search space of possible solutions and determine the optimum solution via exact methods, such as integer linear programming (ILP), via a configurable, scalarised objective function that takes conflicting goals into account. However, the determination of suitable configuration parameters and runtime efficiency improvements are still an open issue, which is commonly addressed by using heuristics instead of exact methods. We investigate on whether it is beneficial to apply heuristics to solve concurrent model synchronisation problems. First, a multiobjective evolutionary algorithm is used for small instances for which all pareto-optimal solutions can be presented to a user to select the best one. Second, for larger models, we propose a method to determine suitable weightings for aggregating all objectives into a single function. Finally, these insights are used to recommend a strategy for determining solutions of satisfying quality within an acceptable amount of time.

Due to the complexity of designing vehicle products and the inherent uncertainties in their operating environments, ensuring the safety of their Advanced Driver Assistance Systems (ADASs) becomes crucial. Especially, very minor changes to a vehicle design, for instance due to production errors or component degradation, might lead to failures of ADASs and, therefore, catastrophic consequences such as collision occurrences. Motivated by this, we propose a multi-objective search-based approach (employing NSGA-II) to find minimum changes to the configuration of a set of configurable parameters of a vehicle design, such that the collision probability is maximized, consequently leading to a reversal change in its safety. We conducted experiments, in a vehicle driving simulator, to evaluate the effectiveness of our approach. Results show that our approach with NSGA-II significantly outperforms the random search. Moreover, based on the detailed analyses of the results, we identify some parameters for which minor changes to their values lead the vehicle into collisions, and demonstrated the importance of studying the configuration of multiple parameters in a single search and the impact of their interactions on causing collisions.