Inputs from Hell: Learning Input Distributions for Grammar-Based Test Generation
Thu 27 May 2021 06:50 - 07:10 at Blended Sessions Room 1 - 2.5.1. Testing: Automatic Test Generation
When a program has been tested on some sample input(s), what additional input does one test next? To further test the program, one needs to construct inputs that cover (new) input features, in a manner that is different from the initial samples.
This paper presents a novel test generation approach that employs context-free grammars to learn the production probabilities of input elements from sample inputs. Using the grammars as input parsers, we show how to learn input distributions from sample inputs, allowing to create “common inputs” that are similar to the sample. By inverting the learned probabilities, we can create “uncommon inputs” that are dissimilar to the sample.
Our evaluation of these approaches on three input formats show that “common inputs” reproduced 96% of the methods induced by the samples and the “uncommon inputs” covered different methods from the samples for almost all subjects (95%).
Wed 26 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
18:50 - 19:50
2.5.1. Testing: Automatic Test GenerationJournal-First Papers / Technical Track at Blended Sessions Room 1 +12h
Chair(s): José Miguel Rojas University of Leicester, UK
|Inputs from Hell: Learning Input Distributions for Grammar-Based Test GenerationJournal-First|
Ezekiel Soremekun SnT, University of Luxembourg, Esteban Pavese Humboldt University of Berlin, Nikolas Havrikov CISPA, Germany, Lars Grunske Humboldt University of Berlin, Andreas Zeller CISPA Helmholtz Center for Information SecurityLink to publication DOI Pre-print Media Attached
|Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?Technical Track|
Song Wang York University, Nishtha Shrestha York University, Abarna Kucheri Subburaman York University, Junjie Wang Institute of Software, Chinese Academy of Sciences, Moshi Wei York University, Nachiappan Nagappan Microsoft ResearchLink to publication Pre-print Media Attached
|Using Relative Lines of Code to Guide Automated Test Generation for PythonJournal-First|
Josie Holmes Northern Arizona University, Iftekhar Ahmed University of California, Irvine, Caius Brindescu Oregon State University, Rahul Gopinath CISPA Helmholtz Center for Information Security, He Zhang Nanjing University, Alex Groce Northern Arizona UniversityPre-print Media Attached