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Summary: Covering all the possible paths of the graphical user interface (GUI) with test scripts would take too much effort and result in serious maintenance issues. We propose complementing scripted testing with scriptless test automation using the open‐source testar tool. This paper gives a comprehensive overview of testar and its latest extensions together with the ongoing and future research. With this paper, we hope we can help and encourage other researchers to use testar for their GUI testing‐related research and pave the way for an international research agenda in GUI testing built upon stable and open‐source infrastructure. [ABSTRACT FROM AUTHOR]

Traversal-based automated software testing involves testing an application via its graphical user interface (GUI) and thereby taking the user’s point of view and executing actions in a human-like manner. These actions are decided on the fly, as the software under test (SUT) is being run, as opposed to being set up in the form of a sequence prior to the testing, a sequence that is then used to exercise the SUT. In practice, random choice is commonly used to decide which action to execute at each state (a procedure commonly referred to as monkey testing), but a number of alternative mechanisms have also been proposed in the literature. Here we propose using genetic programming (GP) to evolve such an action selection strategy, defined as a list of IF-THEN rules. Genetic programming has proved to be suited for evolving all sorts of programs, and rules in particular, provided adequate primitives (functions and terminals) are defined. These primitives must aim to extract the most relevant information from the SUT and the dynamics of the testing process. We introduce a number of such primitives suited to the problem at hand and evaluate their usefulness based on various metrics. We carry out experiments and compare the results with those obtained by random selection and also by Q-learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments. The analysis shows the potential of GP to evolve action selection strategies. [ABSTRACT FROM AUTHOR]

Models are key artifacts in Model-Driven Development (MDD) methods. To produce high-quality software by using MDD methods, quality assurance of models is of paramount importance. To evaluate the quality of models, defect detection is considered a suitable approach and is usually applied using reading techniques. However, these reading techniques have limitations and constraints, and new techniques are required to improve the efficiency at finding as many defects as possible. This article presents a case study that has been carried out to evaluate the use of a Functional Size Measurement (FSM) procedure in the detection of defects in models of an MDD environment. To do this, we compare the defects and the defect types found by an inspection group with the defects and the defect types found by the FSM procedure. The results indicate that the FSM is useful since it finds all the defects related to a specific defect type, it finds different defect types than an inspection group, and it finds defects related to the correctness and the consistency of the models. [ABSTRACT FROM AUTHOR]

During the past years, evolutionary testing research has reported encouraging results for automated functional (i.e. black-box) testing. However, despite promising results, these techniques have hardly been applied to complex, real-world systems and as such, little is known about their scalability, applicability, and acceptability in industry. In this paper, we describe the empirical setup used to study the use of evolutionary functional testing in industry through two case studies, drawn from serial production development environments at Daimler and Berner & Mattner Systemtechnik, respectively. Results of the case studies are presented, and research questions are assessed based on them. In summary, the results indicate that evolutionary functional testing in an industrial setting is both scalable and applicable. However, the creation of fitness functions is time-consuming. Although in some cases, this is compensated by the results, it is still a significant factor preventing functional evolutionary testing from more widespread use in industry. [ABSTRACT FROM AUTHOR]

Evolutionary structural testing has been researched and promising results have been presented. However, it has hardly been applied to real-world complex systems and as such, little is known about the scalability, applicability and acceptability of it in an industrial setting. The European project EvoTest (IST-33472) team has been working from 2006 till 2009 to improve this situation and this paper informs about the results. We start with an overview of tools and techniques which we have developed for automated evolutionary structural testing. Subsequently, we describe the empirical setup used to study the applicability of evolutionary structural testing in industry through two case studies. The test objects used for the studies are selected functions (handwritten and generated) from production systems at Daimler and Berner & Mattner Systemtechnik (BMS) like, for example, Rear Window Defroster, Global Powertrain Engine Controller, Window Lift Control System, etc. The results of the case studies are described and research questions are assessed based on the obtained results. In summary, the results indicate that evolutionary structural testing in an industrial setting is worthwhile and profitable. Hardly any detailed knowledge of evolutionary computation is required to search for interesting test data. The case studies also research the benefits of using techniques like automated parameter tuning and search space smoothing. [ABSTRACT FROM AUTHOR]

Usability is currently a key feature for developing quality systems. A system that satisfies all the functional requirements can be strongly rejected by end-users if it presents usability problems. End-users demand intuitive interfaces and an easy interaction in order to simplify their work. The first step in developing usable systems is to determine whether a system is or is not usable. To do this, there are several proposals for measuring the system usability. Most of these proposals are focused on the final system and require a large amount of resources to perform the evaluation (end-users, video cameras, questionnaires, etc.). Usability problems that are detected once the system has been developed involve a lot of reworking by the analyst since these changes can affect the analysis, design, and implementation phases. This paper proposes a method to minimize the resources needed for the evaluation and reworking of usability problems. We propose an early usability evaluation that is based on conceptual models. The analyst can measure the usability of attributes that depend on conceptual primitives. This evaluation can be automated taking as input the conceptual models that represent the system abstractly. [ABSTRACT FROM AUTHOR]