We present a technique to make applications resilient to failures. This technique is intended to maintain a faulty application functional in the field while the developers work on permanent and radical fixes. We target field failures in applications built on reusable components. In particular, the technique exploits the intrinsic redundancy of those components by identifying workarounds consisting of alternative uses of the faulty components that avoid the failure. The technique is currently implemented for Java applications but makes little or no assumptions about the nature of the application, and works without interrupting the execution flow of the application and without restarting its components. We demonstrate and evaluate this technique on four mid-size applications and two popular libraries of reusable components affected by real and seeded faults. In these cases the technique is effective, maintaining the application fully functional with between 19% and 48% of the failure-causing faults, depending on the application. The experiments also show that the technique incurs an acceptable runtime overhead in all cases.

Symbolic execution is a powerful program analysis technique that systematically explores multiple program paths. However, despite important technical advances, symbolic execution often struggles to reach deep parts of the code due to the well-known path explosion problem and constraint solving limitations. In this paper, we propose chopped symbolic execution, a novel form of symbolic execution that allows users to specify uninteresting parts of the code to exclude during the analysis, thus only targeting the exploration to paths of importance. However, the excluded parts are not summarily ignored, as this may lead to both false positives and false negatives. Instead, they are executed lazily, when their effect may be observable by code under analysis. Chopped symbolic execution leverages various on-demand static analyses at runtime to automatically exclude code fragments while resolving their side effects, thus avoiding expensive manual annotations and imprecision. Our preliminary results show that the approach can effectively improve the effectiveness of symbolic execution in several different scenarios, including failure reproduction and test suite augmentation.

Despite the recent improvements in automatic test case generation, handling complex data structures as test inputs is still an open problem. Search-based approaches can generate sequences of method calls that instantiate structured inputs to exercise a relevant portion of the code, but fall short in building inputs to execute program elements whose reachability is determined by the structural features of the input structures themselves. Symbolic execution techniques can effectively handle structured inputs, but do not identify the sequences of method calls that instantiate the input structures through legal interfaces. In this paper, we propose a new approach to automatically generate test cases for programs with complex data structures as inputs. We use symbolic execution to generate path conditions that characterise the dependencies between the program paths and the input structures, and convert the path conditions to optimisation problems that we solve with search-based techniques to produce sequences of method calls that instantiate those inputs. Our preliminary results show that the approach is indeed effective in generating test cases for programs with complex data structures as inputs, thus opening a promising research direction. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Formal software verification; KEYWORDSAutomatic test case generation, Symbolic execution, Search-based software engineering ACM Reference format:

Despite the recent advances in automatic test generation, testers must still write test oracles manually. If formal specifications are available, it might be possible to use decision procedures derived from those specifications. We present a technique that is based on a form of specification but also leverages more information from the system under test. We assume that the system under test is somewhat redundant, in the sense that some operations are designed to behave like others but their executions are different. Our experience in this and previous work indicates that this redundancy exists and is easily documented. We then generate oracles by cross-checking the execution of a test with the same test in which we replace some operations with redundant ones. We develop this notion of cross-checking oracles into a generic technique to automatically insert oracles into unit tests. An experimental evaluation shows that cross-checking oracles, used in combination with automatic test generation techniques, can be very effective in revealing faults, and that they can even improve good hand-written test suites.

Despite significant recent advances, the effectiveness of symbolic execution is limited when used to test complex, real-world software. One of the main scalability challenges is related to constraint solving: large applications and long exploration paths lead to complex constraints, often involving big arrays indexed by symbolic expressions. In this paper, we propose a set of semantics-preserving transformations for array operations that take advantage of contextual information collected during symbolic execution. Our transformations lead to simpler encodings and hence better performance in constraint solving. The results we obtain are encouraging: we show, through an extensive experimental analysis, that our transformations help to significantly improve the performance of symbolic execution in the presence of arrays. We also show that our transformations enable the analysis of new code, which would be otherwise out of reach for symbolic execution. CCS CONCEPTS • Software and its engineering → Software testing and debugging;