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ICSE 2021
Mon 17 May - Sat 5 June 2021

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%).

Conference Day
Wed 26 May

Displayed 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 RojasUniversity of Leicester, UK
18:50
20m
Paper
Inputs from Hell: Learning Input Distributions for Grammar-Based Test GenerationJournal-First
Journal-First Papers
Ezekiel SoremekunSnT, University of Luxembourg, Esteban PaveseHumboldt University of Berlin, Nikolas HavrikovCISPA, Germany, Lars GrunskeHumboldt University of Berlin, Andreas ZellerCISPA Helmholtz Center for Information Security
Link to publication DOI Pre-print Media Attached
19:10
20m
Paper
Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?Technical Track
Technical Track
Song WangYork University, Nishtha ShresthaYork University, Abarna Kucheri SubburamanYork University, Junjie WangInstitute of Software, Chinese Academy of Sciences, Moshi WeiYork University, Nachiappan NagappanMicrosoft Research
Link to publication Pre-print Media Attached
19:30
20m
Paper
Using Relative Lines of Code to Guide Automated Test Generation for PythonJournal-First
Journal-First Papers
Josie HolmesNorthern Arizona University, Iftekhar AhmedUniversity of California, Irvine, Caius BrindescuOregon State University, Rahul GopinathCISPA Helmholtz Center for Information Security, He ZhangNanjing University, Alex GroceNorthern Arizona University
Pre-print Media Attached

Conference Day
Thu 27 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

06:50 - 07:50
2.5.1. Testing: Automatic Test GenerationTechnical Track / Journal-First Papers at Blended Sessions Room 1
06:50
20m
Paper
Inputs from Hell: Learning Input Distributions for Grammar-Based Test GenerationJournal-First
Journal-First Papers
Ezekiel SoremekunSnT, University of Luxembourg, Esteban PaveseHumboldt University of Berlin, Nikolas HavrikovCISPA, Germany, Lars GrunskeHumboldt University of Berlin, Andreas ZellerCISPA Helmholtz Center for Information Security
Link to publication DOI Pre-print Media Attached
07:10
20m
Paper
Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?Technical Track
Technical Track
Song WangYork University, Nishtha ShresthaYork University, Abarna Kucheri SubburamanYork University, Junjie WangInstitute of Software, Chinese Academy of Sciences, Moshi WeiYork University, Nachiappan NagappanMicrosoft Research
Link to publication Pre-print Media Attached
07:30
20m
Paper
Using Relative Lines of Code to Guide Automated Test Generation for PythonJournal-First
Journal-First Papers
Josie HolmesNorthern Arizona University, Iftekhar AhmedUniversity of California, Irvine, Caius BrindescuOregon State University, Rahul GopinathCISPA Helmholtz Center for Information Security, He ZhangNanjing University, Alex GroceNorthern Arizona University
Pre-print Media Attached