A Method Level Test Generation Framework for Debugging Big Data Applications

Published

January 24, 2019

Author(s)

Huadong Feng, Jagan Chandrasekaran, Yu Lei, Raghu N. Kacker, D. Richard Kuhn

Abstract

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, US

Conference Title

2018 IEEE International Conference on Big Data

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/BigData.2018.8622248

Local Download

Keywords

Testing, Unit Testing, Big Data Application Testing, Test Generation, Test Reduction, Debugging, Mutation Testing

Citation

Feng, H. , Chandrasekaran, J. , Lei, Y. , Kacker, R. and Kuhn, D. (2019), A Method Level Test Generation Framework for Debugging Big Data Applications, 2018 IEEE International Conference on Big Data, Seattle, WA, US, [online], https://doi.org/10.1109/BigData.2018.8622248, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=927138 (Accessed April 19, 2024)