TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution
Real-world test suites are often complex and may have thousands of test cases. In this scenario, it is not easy to spot what values are actually covered by the tests. Having access to every tested value of a test suite would provide the basis to (1) assess the quality of tested data and (2) have actionable information to improve them. In this paper, we propose TestDossier, a dataset of tested values automatically extracted from the execution of Python tests. To collect runtime data, we run an instrumented version of the tests, monitoring the test execution, and extracting argument and variable values. We monitored the test suites of 15 Python Standard Libraries to create the dataset. TestDossier contains 1,234 distinct argument/variable names and 133,169 distinct values, leading to a total of 12,9M individual values. We envision that our dataset can help developers detect rarely tested values, untested values, and variations of tested values. We also foresee that our dataset can support novel empirical studies in the context of software testing, for example, it can expose the diversity of the tested data.
Andre Hora is currently a professor in the Department of Computer Science at UFMG, Brazil. He received his PhD in Computer Science at the University of Lille, France. He was a Postdoctoral researcher at the ASERG group. He worked as a software engineer at Inria (Lille, France) and was research intern at Siemens (Erlangen, Germany).
Mon 15 AprDisplayed time zone: Lisbon change
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15:07 4mTalk | TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution Data and Tool Showcase Track Andre Hora UFMG Pre-print Media Attached | ||
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