Adaptive Test Case Selection - Datasets
Data sets to evaluate test case prioritization and selection, finding test cases likely to fail.
Main Characteristic
These data sets provide historical information about test case executions and their results. It can be used to evaluate test case prioritization and selection methods, finding test cases most likely to fail during their next execution.
Research areas
Verifiable AI
Technical Categories
Machine learning
Last updated
09.03.2022 - 10:03
Detailed Description
Number of elements: 1300000
Overall size of the datatsets: 3
Additional information: Test cases are defined by their execution duration, their previous last execution time and results of their recent executions. Two of these data sets are provided by ABB Robotics Norway, the other one was published by Google and is included here in its converted form.
References: For the original GSDTSR data set repository see:
- Sebastian Elbaum, Andrew Mclaughlin, and John Penix, "The Google Dataset of Testing Results", https://code.google.com/p/google-shared-dataset-of-test-suite-results, 2014
Documents
Trustworthy AI
The ATCS dataset does not violate the guidelines for Trustworthy AI.
GDPR Requirements
The datasets do not contain personal information or a link to the involved developers.