Semantics-based Automated Web Testing

We present TAO, a software testing tool performing automated test and oracle generation based on a semantic approach. TAO entangles grammar-based test generation with automated semantics evaluation using a denotational semantics framework. We show how TAO can be incorporated with the Selenium automation tool for automated web testing, and how TAO can be further extended to support automated delta debugging, where a failing web test script can be systematically reduced based on grammar-directed strategies. A real-life parking website is adopted throughout the paper to demonstrate the effectivity of our semantics-based web testing approach.Comment: In Proceedings WWV 2015, arXiv:1508.0338

Simplifying Contract-Violating Traces

Contract conformance is hard to determine statically, prior to the deployment of large pieces of software. A scalable alternative is to monitor for contract violations post-deployment: once a violation is detected, the trace characterising the offending execution is analysed to pinpoint the source of the offence. A major drawback with this technique is that, often, contract violations take time to surface, resulting in long traces that are hard to analyse. This paper proposes a methodology together with an accompanying tool for simplifying traces and assisting contract-violation debugging.Comment: In Proceedings FLACOS 2012, arXiv:1209.169

Using hardware performance counters for fault localization

In this work, we leverage hardware performance counters-collected data as abstraction mechanisms for program executions and use these abstractions to identify likely causes of failures. Our approach can be summarized as follows: Hardware counters-based data is collected from both successful and failed executions, the data collected from the successful executions is used to create normal behavior models of programs, and deviations from these models observed in failed executions are scored and reported as likely causes of failures. The results of our experiments conducted on three open source projects suggest that the proposed approach can effectively prioritize the space of likely causes of failures, which can in turn improve the turn around time for defect fixes

Cause Clue Clauses: Error Localization using Maximum Satisfiability

Much effort is spent everyday by programmers in trying to reduce long, failing execution traces to the cause of the error. We present a new algorithm for error cause localization based on a reduction to the maximal satisfiability problem (MAX-SAT), which asks what is the maximum number of clauses of a Boolean formula that can be simultaneously satisfied by an assignment. At an intuitive level, our algorithm takes as input a program and a failing test, and comprises the following three steps. First, using symbolic execution, we encode a trace of a program as a Boolean trace formula which is satisfiable iff the trace is feasible. Second, for a failing program execution (e.g., one that violates an assertion or a post-condition), we construct an unsatisfiable formula by taking the trace formula and additionally asserting that the input is the failing test and that the assertion condition does hold at the end. Third, using MAX-SAT, we find a maximal set of clauses in this formula that can be satisfied together, and output the complement set as a potential cause of the error. We have implemented our algorithm in a tool called bug-assist for C programs. We demonstrate the surprising effectiveness of the tool on a set of benchmark examples with injected faults, and show that in most cases, bug-assist can quickly and precisely isolate the exact few lines of code whose change eliminates the error. We also demonstrate how our algorithm can be modified to automatically suggest fixes for common classes of errors such as off-by-one.Comment: The pre-alpha version of the tool can be downloaded from http://bugassist.mpi-sws.or

Poster: Debugging Inputs

Program failures are often caused by invalid inputs, for instance due to input corruption. To obtain the passing input, one needs to debug the data. In this paper we present a generic technique called ddmax that (1) identifies which parts of the input data prevent processing, and (2) recovers as much of the (valuable) input data as possible. To the best of our knowledge, ddmax is the first approach that fixes faults in the input data without requiring program analysis. In our evaluation, ddmax repaired about 69% of input files and recovered about 78% of data within one minute per input