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A Method Level Test Generation Framework for Debugging Big Data Applications



Huadong Feng, Jagan Chandrasekaran, Yu Lei, Raghu N. Kacker, David R. Kuhn


When a failure occurs in a big data application, debugging with the original dataset can be difficult due to the large amount of data being processed. This paper introduces a framework for effectively generating method-level tests to facilitate debugging of big data applications. This is achieved by running a big data application with the original dataset and by recording the inputs to a small number of method executions, which we refer to as method-level tests, that preserves certain code coverage, e.g., edge coverage. The inputs of these method-level tests are further reduced if needed, while maintaining code coverage. When debugging, a developer could inspect the execution of these method-level tests, instead of the entire program execution with the original dataset, which could be time-consuming. We implemented the framework and applied the framework to seven algorithms in the WEKA tool. The initial results show that a small number of method-level tests are sufficient to preserve code coverage. Furthermore, these tests could kill between 57.58% to 91.43% of the mutants generated using a mutation testing tool. This suggests that the framework could significantly reduce the efforts required for debugging big data applications.
Conference Dates
December 10-13, 2018
Conference Location
Seattle, WA
Conference Title
2018 IEEE International Conference on Big Data


Testing, Unit Testing, Big Data Application Testing, Test Generation, Test Reduction, Debugging, Mutation Testing
Created January 24, 2019, Updated February 22, 2019