Biblio

Dynamic tainting is an important part of modern software engineering research. State-of-the-art tools for debugging, bug detection and program analysis make use of this technique. Nonetheless, the research area based on dynamic tainting still has open questions, among others the automatic generation of program inputs. My proposed work concentrates on the use of dynamic tainting for test case generation. The goal is the generation of complex and valid test inputs from scratch. Therefore, I use byte level taint information enhanced with additional static and dynamic program analysis. This information is used in an evolutionary algorithm to create new offsprings and mutations. Concretely, instead of crossing and mutating the whole input randomly, taint information can be used to define which parts of the input have to be mutated. Furthermore, the taint information may also be used to define evolutionary operators. Eventually, the evolutionary algorithm is able to generate valid inputs for a program. Such inputs can be used together with the taint information for further program analysis, e.g. the generation of input grammars.

The rising popularity of Android and the GUI-driven nature of its apps have motivated the need for applicable automated GUI testing techniques. Although exhaustive testing of all possible combinations is the ideal upper bound in combinatorial testing, it is often infeasible, due to the combinatorial explosion of test cases. This paper presents TrimDroid, a framework for GUI testing of Android apps that uses a novel strategy to generate tests in a combinatorial, yet scalable, fashion. It is backed with automated program analysis and formally rigorous test generation engines. TrimDroid relies on program analysis to extract formal specifications. These speci- fications express the app’s behavior (i.e., control flow between the various app screens) as well as the GUI elements and their dependencies. The dependencies among the GUI elements comprising the app are used to reduce the number of combinations with the help of a solver. Our experiments have corroborated TrimDroid’s ability to achieve a comparable coverage as that possible under exhaustive GUI testing using significantly fewer test cases.

Pervasiveness of smartphones and the vast number of corresponding apps have underlined the need for applicable automated software testing techniques. A wealth of research has been focused on either unit or GUI testing of smartphone apps, but little on automated support for end-to-end system testing. This paper presents SIG-Droid, a framework for system testing of Android apps, backed with automated program analysis to extract app models and symbolic execution of source code guided by such models for obtaining test inputs that ensure covering each reachable branch in the program. SIG-Droid leverages two automatically extracted models: Interface Model and Behavior Model. The Interface Model is used to find values that an app can receive through its interfaces. Those values are then exchanged with symbolic values to deal with constraints with the help of a symbolic execution engine. The Behavior Model is used to drive the apps for symbolic execution and generate sequences of events. We provide an efficient implementation of SIG-Droid based in part on Symbolic PathFinder, extended in this work to support automatic testing of Android apps. Our experiments show SIG-Droid is able to achieve significantly higher code coverage than existing automated testing tools targeted for Android.