GOES-R AIT: Development of Standard Test Data Sets for Routine Testing

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Monday, 5 January 2015
Jonathan E. Wrotny, I.M. Systems Group, College Park, MD; and Z. Zhang, S. Sampson, W. Wolf, and W. Straka III

The GOES-R Algorithm Working Group (AWG) Algorithm Integration Team (AIT) maintains and updates a data processing framework for the GOES-R data processing algorithms. This system provides an environment for algorithm development and testing along with the ability to process multiple algorithms in sequence with product precedence. Most of the AWG algorithms are able to process on different satellite data sets and simulated data. The AIT maintains a standard test data set which is used for routine testing of the AWG algorithms. As modifications to the framework data processing system are made over time due to new algorithms, new input data for current algorithms, or other software updates and fixes, it is important that the system as a whole remains stable in terms of the algorithm output products, i.e. changes in one part of the Framework do not inadvertently affect other parts. In particular, software updates to an algorithm should not alter that algorithm's outputs or updates to an algorithm should not affect another algorithm's outputs. Routine regression testing is undertaken by the AIT to ensure that the Framework system is stable in these regards by comparing algorithm output products before and after updates to the Framework are made.

The test data set is chosen so that algorithm coverage (i.e. the number of lines of source code) is maximized during algorithm processing. Based on the design of the science algorithms and their dependencies on their inputs (e.g. geographic, solar conditions, etc.), satellite data at different times of day and seasonal conditions is selected for each satellite dataset. In order to optimize the selection of the test data, the standard GNU utility ‘Gcov' is used to determine which test data maximizes algorithm coverage. The ‘Gcov' software quantifies algorithm coverage by identifying the number of times each line of source code is executed during processing. Test data that maximizes coverage for each AWG algorithm is chosen for the standard test data set with the goal of overlapping input data for multiple algorithms in order to minimize storage of test data and save computer resources during algorithm processing. This poster describes the methods that were used to create the standard test data set for the AWG algorithms including how ‘Gcov' was utilized.