Publications

Recently, software verification is being used to prove the presence of contradictions in source code and thus reveal potential weaknesses in the code or provide assistance to the compiler optimization. Compared to verification of correctness properties, the translation from source code to logic can be very simple and thus easy to solve by automated theorem provers. In this paper, we present a translation of Java into logic that is suitable for proving the presence of contradictions in code. We show that the translation, which is based on the Jimple language, can be used to analyze real-world programs, and discuss some issues that arise from differences between Java code and its bytecode

 We present Joogie, a tool that detects infeasible code in Java programs. Infeasible code is code that does not occur on feasible control-flow paths and thus has no feasible execution. Infeasible code comprises many errors detected by static analysis in modern IDEs such as guaranteed null-pointer dereference or unreachable code. Unlike existing techniques, Joogie identifies infeasible code by proving that a particular statement cannot occur on a terminating execution using techniques from static verification. Thus, Joogie is able to detect infeasible code which is overlooked by existing tools. Joogie works fully automatic, it does not require user- provided specifications and (almost) never produces false warnings.

 This chapter gives an overview of the recent advances in GUItesting.Considering the increasing popularity and fast software development cycles (e.g.,desktop and mobile applications), GUI testing gains more importance as it allows us to verify the behavior of a system from the user’s perspective.Thus, it can quickly uncover relevant bugs, which a user could face. Traditional capture-replay GUI testing approaches do not meet the demands of developers anymore. Therefore, there is an increasing research activity in model-based GUI testing, where the user interaction behavior is simulated using a graph-based model.In the following, we outline different graphical notations to describe feasible user interactions, and methods to generate and execute test cases from these models. We discuss the benefits and limitations of the state-of-the-art in GUI testing research and give a brief outlook about new trends and possibilities to improve the GUI testing automation.

Graphical user interfaces (GUIs) are a common way to interact with software. To ensure the quality of such software it is important to test the possible interactions with its user interface. GUI testing is a challenging task as they can allow, in general, infinitely many different sequences of interactions with the software. As it is only possible to test a limited amount of possible user interactions, it is crucial for the quality of GUI testing to identify relevant sequences and avoid improper ones. In this paper we propose a model for better GUI testing. Our model is created based on two observations. It is a common case that different user interactions result in the execution of the same code fragments. That is, it is sufficient to test only interactions that execute different code fragments. Our second observation is that user interactions are contextsensitive. That is, the control flow that is taken in a program fragment handling a user interaction depends on the order of some preceding user interactions. We show that these observations are relevant in practice. We present a preliminary implementation that utilizes these observations for test case generation.

 Graphical user interfaces (GUIs) are a common way to interact with software. To ensure the quality of such software it is important to test the possible interactions with its user interface. GUI testing is a challenging task as they can allow, in general, infinitely many different sequences of interactions with the software. As it is only possible to test a limited amount of possible user interactions, it is crucial for the quality of GUI testing to identify relevant sequences and avoid improper ones. In this paper we propose a model for better GUI testing. Our model is created based on two observations. It is a common case that different user interactions result in the execution of the same code fragments. That is, it is sufficient to test only interactions that execute different code fragments. Our second observation is that user interactions are context-sensitive. That is, the control flow that is taken in a program fragment handling a user interaction depends on the order of some preceding user interactions. We show that these observations are relevant in practice. We present a preliminary implementation that utilizes these observations for test case generation.