Linux Testing made better with DATA
Tux Theatre | Sat 23 Jan 3:45 p.m.–4 p.m.
Presented by
-
Nageswara Sastry (nasastry@in.ibm.com) is an Advisory Software Engineer at Linux Technology Centre, IBM India. He is passionate about new technologies and learning. He is having 18+ years of Test development experience in Linux and related areas. He was speaker at different conferences namely Linux World Conference, World conference of Next Generation Testing, NFTCon.
Abstract
Data is fuel to any product transformation. Any useful Insights derived from data helps in reducing unnecessary work, doing things faster with more accuracy and efficiency. Linux Testing creates different types of data. The objective of this talk would be to present about how we can use data collected from code coverage to identify redundant test cases, reduce the number of test cases to execute, create dynamic test suite and prioritise test cases. We will show case significant reduction in time and effort for test engineers, developers as well as efficient hardware usage in the mentioned use cases.
Created a simple solution using ctags, code coverage data. In this solution used test case/suite reduction technique named code coverage method, this method helps in reducing 99% of the test cases with out effecting the bug identification capability. From the initial runs in our environment seen 93.5% reduction of test cases. Though the solution is simple, but effective in identifying redundant test cases, reducing number of test cases to execute, what tests to improve, which parts of the code needs new test cases.
Data is fuel to any product transformation. Any useful Insights derived from data helps in reducing unnecessary work, doing things faster with more accuracy and efficiency. Linux Testing creates different types of data. The objective of this talk would be to present about how we can use data collected from code coverage to identify redundant test cases, reduce the number of test cases to execute, create dynamic test suite and prioritise test cases. We will show case significant reduction in time and effort for test engineers, developers as well as efficient hardware usage in the mentioned use cases. Created a simple solution using ctags, code coverage data. In this solution used test case/suite reduction technique named code coverage method, this method helps in reducing 99% of the test cases with out effecting the bug identification capability. From the initial runs in our environment seen 93.5% reduction of test cases. Though the solution is simple, but effective in identifying redundant test cases, reducing number of test cases to execute, what tests to improve, which parts of the code needs new test cases.