University of Minnesota
Software Engineering Center

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Dongjiang You

Student/Research Assistant
Dongjiang You graduated with a Ph.D. in Computer Science from the University of Minnesota in 2016. He was a Research Assistant in the Critical Systems Group working with Prof. Mats Heimdahl and Dr. Sanjai Rayadurgam. Before that, he worked with Prof. Zhenyu Chen at Nanjing University. His research interests are broadly in the area of software engineering, including automated test generation, program analysis, symbolic execution, and model checking. He received his Bachelor's degree in Software Engineering from Nanjing University in 2011.

Recent Publications

Practical Aspects of Building a Constrained Random Test Framework for Safety-critical Embedded Systems

In the safety-critical embedded system industry, one of the key challenges is to demonstrate the robustness and dependability of the product prior to market release, which is typically done using various verification and validation (V&V) strategies. Directed verification testing is a common strategy that performs black-box testing at the system level; however, it only samples a small set of specific system behaviors and requires heavily manual effort.

Observable Modified Condition/Decision Coverage

In many critical systems domains, test suite adequacy is currently measured using structural coverage metrics over the source code. Of particular interest is the modified condition/decision coverage (MC/DC) criterion required for, e.g., critical avionics systems. In previous investigations we have found that the efficacy of such test suites is highly dependent on the structure of the program under test and the choice of variables monitored by the oracle.

Mutation Selection: Some Could be Better than All

In previous research, many mutation selection techniques have been proposed to reduce the cost of mutation analysis. After a mutant subset is selected, researchers could obtain a test suite which can detect all mutants in the mutant subset. Then they run all mutants over this test suite, and the detection ratio to all mutants is used to evaluate the effectiveness of mutation selection techniques. The higher the ratio is, the better this selection technique is. Obviously, this measurement has a presumption that the set of all mutants is the best to evaluate test cases.