University of Minnesota
Software Engineering Center

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Critical Systems Research Group

The Critical Systems Research Group’s (CriSys) research interests are in the general area of software engineering; in particular, software development for critical software applications — applications where incorrect operation of the software could lead to loss of life, substantial material or environmental damage, or large monetary losses. The long-term goal of our research activities is the development of a comprehensive framework for the development of software for critical software systems. Our work has focused on some of the most difficult and least understood aspects of software development—requirements specification and validation/verification.

Recent Publications

Automated Oracle Data Selection Support

The choice of test oracle—the artifact that determines whether an application under test executes correctly—can significantly impact the effectiveness of the testing process. However, despite the prevalence of tools that support test input selection, little work exists for supporting oracle creation. We propose a method of supporting test oracle creation that automatically selects the oracle data—the set of variables monitored during testing—for expected value test oracles. This approach is based on the use of

Automated Steering of Model-Based Test Oracles to Admit Real Program Behaviors

The test oracle—a judge of the correctness of the system under test (SUT)—is a major com- ponent of the testing process. Specifying test oracles is challenging for some domains, such as real-time embedded systems, where small changes in timing or sensory input may cause large behavioral differences. Models of such systems, often built for analysis and simulation, are appealing for reuse as test oracles. These models, however, typically represent an idealized system, abstracting away certain issues such as non-deterministic timing behavior and sensor

A flexible and non-intrusive approach for computing complex structural coverage metrics

Software analysis tools and techniques often leverage structural code coverage information to reason about the dynamic behavior of software. Existing techniques instrument the code with the required structural obligations and then monitor the execution of the compiled code to report coverage. Instrumentation based approaches often incur considerable runtime overhead for complex structural coverage metrics such as Modified Condition/Decision (\mcdc). Code instrumentation, in general, has to be approached with great care to ensure it does not modify the behavior of the original code.