Metaheuristic search techniques have been extensively used to automate the process of generating test cases, and thus providing solutions for a more cost-effective testing process. This approach to test automation, often coined “Search-based Software Testing” (SBST), has been used for a wide variety of test case generation purposes. Since SBST techniques are heuristic by nature, they must be empirically investigated in terms of how costly and effective they are at reaching their test objectives and whether they scale up to realistic development artifacts. However, approaches to empirically study SBST techniques have shown wide variation in the literature. This paper presents the results of a systematic, comprehensive review that aims at characterizing how empirical studies have been designed to investigate SBST cost-effectiveness and what empirical evidence is available in the literature regarding SBST cost-effectiveness and scalability. We also provide a framework that drives the data collection process of this systematic review and can be the starting point of guidelines on how SBST techniques can be empirically assessed. The intent is to aid future researchers doing empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well-designed and executed empirical studies.

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective—key qualities of any industry-grade software—mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering (QSE) as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyze in depth the current state of QSE research. From this analysis, the key areas of QSE are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing QSE.

Model-based testing (MBT) aims at automated, scalable, and systematic testing solutions for complex industrial software systems. To increase chances of adoption in industrial contexts, software systems can be modeled using well-established standards such as the Unified Modeling Language (UML) and the Object Constraint Language (OCL). Given that test data generation is one of the major challenges to automate MBT, we focus on test data generation from OCL constraints in this paper. This endeavor is all the more challenging given the numerous OCL constructs and operations that are designed to facilitate the definition of constraints. Though search-based software testing has been applied to test data generation for white-box testing (e.g., branch coverage), its application to the MBT of industrial software systems has been limited. In this paper, we propose a set of search heuristics targeted to OCL constraints to guide test data generation and automate MBT in industrial applications. We evaluate these heuristics for three search algorithms: Genetic Algorithm, (1+1) Evolutionary Algorithm, and Alternating Variable Method. We empirically evaluate our heuristics using complex artificial problems, followed by empirical analyses of the feasibility of our approach on one industrial system in the context of robustness testing. Our approach is also compared with the most widely referenced OCL solver (UMLtoCSP) in the literature and shows to be significantly more efficient.

Autonomous vehicles must operate safely in their dynamic and continuously-changing environment. However, the operating environment of an autonomous vehicle is complicated and full of various types of uncertainties. Additionally, the operating environment has many configurations, including static and dynamic obstacles with which an autonomous vehicle must avoid collisions. Through various approaches targeting on environment configuration for autonomous vehicles have shown promising results, their effectiveness in dealing with a continuous-changing environment is limited. Thus, it is essential to learn realistic environment configurations ofcontinuously-changing environment, under which an autonomous vehicle should be tested regarding its ability to avoid collisions. Featured with agents dynamically interacting with the environment, Reinforcement Learning (RL) has shown great potential in dealing with complicated problems requiring adapting to the environment. To this end, we present an RL-based environment configuration learning approach, i.e., DeepCollision, which intelligently learns environment configurations that lead an autonomous vehicle to crash.DeepCollision employs Deep Q-Learning as the RL solution, and selects collision probability, as the safety measure, to construct the reward function. We trained four DeepCollision models and conducted an experiment to compare them with two baselines, i.e., random and greedy. Results show that DeepCollision demonstrated significantly better effectiveness in generating collisions compared with the baselines. We also provide recommendations on configuring DeepCollision with the most suitable time interval based on different road structures.