University of Minnesota
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Sanjai Rayadurgam

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Staff Member
Phone Number: 
612-625-0331
Office Location: 
6-202 Keller Hall
Biography: 

Sanjai Rayadurgam is a Research Project Specialist at the University of Minnesota Software Engineering Center. His research interests are in software testing, formal analysis and requirements modeling, with particular focus on safety-critical systems development, where he has significant industrial experience. He earned a B.Sc. in Mathematics from the University of Madras at Chennai, and in Computer Science & Engineering, an M.E. from the Indian Institute of Science at Bangalore and a Ph.D. from the University of Minnesota at Twin Cities. He is a member of IEEE and ACM.

Recent Publications

Representation of Confidence in Assurance Cases using the Beta Distribution

Assurance cases are used to document an argument that a system---such as a critical software system---satisfies some desirable property (e.g., safety, security, or reliability). Demonstrating high confidence that the claims made based on an assurance case can be trusted is crucial to the success of the case. Researchers have proposed quantification of confidence as a Baconian probability ratio of eliminated concerns about the assurance case to the total number of identified concerns.

Executing Model-based Tests on Platform-specific Implementations

Model-based testing of embedded real-time systems is challenging because platform-specific details are often abstracted away to make the models amenable to various analyses. Testing an implementation to expose non-conformance to such a model requires reconciling differences arising from these abstractions. Due to stateful behavior, naive comparisons of model and system behaviors often fail causing numerous false positives.

Efficient Observability-based Test Generation by Dynamic Symbolic Execution

Structural coverage metrics have been widely used to measure test suite adequacy as well as to generate test cases. In previous investigations, we have found that the fault-finding effectiveness of tests satisfying structural coverage criteria is highly dependent on program syntax – even if the faulty code is exercised, its effect may not be observable at the output. To address these problems, observability-based coverage metrics have been defined.

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