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ICSE 2021
Mon 17 May - Sat 5 June 2021

This program is tentative and subject to change.

Mutation testing is a well-established technique for assessing a test suite’s quality by injecting artificial faults into production code. In recent years, mutation testing has been extended to machine learning (ML) systems, and deep learning (DL) in particular; researchers have proposed approaches, tools, and statistically sound heuristics to determine whether mutants in DL systems are killed or not. However, as we will argue in this work, questions can be raised to what extent currently used mutation testing techniques in DL are actually in line with the classical interpretation of mutation testing. We observe that ML model development resembles a test-driven development (TDD) process, in which a training algorithm (‘programmer’) generates a model (program) that fits the data points (test data) to labels (implicit assertions), up to a certain threshold. However, considering proposed mutation testing techniques for ML systems under this TDD metaphor, in current approaches, the distinction between production and test code is blurry, and the realism of mutation operators can be challenged. We also consider the fundamental hypotheses underlying classical mutation testing: the competent programmer hypothesis and coupling effect hypothesis. As we will illustrate, these hypotheses do not trivially translate to ML system development, and more conscious and explicit scoping and concept mapping will be needed to truly draw parallels. Based on our observations, we propose several action points for better alignment of mutation testing techniques for ML with paradigms and vocabularies of classical mutation testing.

This program is tentative and subject to change.

Thu 27 May
Times are displayed in time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

11:50 - 13:05
3.2.4. Mutation Testing: General IssuesNIER - New Ideas and Emerging Results / Journal-First Papers / Technical Track at Blended Sessions Room 4 +12h
Chair(s): Claudia AyalaUniversitat Politècnica de Catalunya, Spain, Sigrid EldhEricsson, Sweden
11:50
20m
Paper
Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction StrategiesJournal-First
Journal-First Papers
Giovani GuizzoUniversity College London, Federica SarroUniversity College London, Jens KrinkeUniversity College London, Silvia Regina VergilioFederal University of Paraná
Link to publication DOI Pre-print
12:10
15m
Short-paper
What Are We Really Testing in Mutation Testing for Machine Learning? A Critical ReflectionNIER
NIER - New Ideas and Emerging Results
Annibale PanichellaDelft University of Technology, Cynthia C. S. LiemDelft University of Technology
Pre-print
12:25
20m
Paper
MuDelta: Delta-Oriented Mutation Testing at Commit TimeTechnical Track
Technical Track
Wei MaSnT, University of Luxembourg, Thierry Titcheu ChekamUniversity of Luxembourg (SnT), Mike PapadakisUniversity of Luxembourg, Luxembourg, Mark HarmanUniversity College London
Pre-print
12:45
20m
Paper
Does mutation testing improve testing practices?Technical Track
Technical Track
Goran PetrovićGoogle Inc, Marko IvankovićGoogle Inc, Gordon FraserUniversity of Passau, René JustUniversity of Washington
Pre-print
23:50 - 01:05
23:50
20m
Paper
Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction StrategiesJournal-First
Journal-First Papers
Giovani GuizzoUniversity College London, Federica SarroUniversity College London, Jens KrinkeUniversity College London, Silvia Regina VergilioFederal University of Paraná
Link to publication DOI Pre-print
00:10
15m
Short-paper
What Are We Really Testing in Mutation Testing for Machine Learning? A Critical ReflectionNIER
NIER - New Ideas and Emerging Results
Annibale PanichellaDelft University of Technology, Cynthia C. S. LiemDelft University of Technology
Pre-print
00:25
20m
Paper
MuDelta: Delta-Oriented Mutation Testing at Commit TimeTechnical Track
Technical Track
Wei MaSnT, University of Luxembourg, Thierry Titcheu ChekamUniversity of Luxembourg (SnT), Mike PapadakisUniversity of Luxembourg, Luxembourg, Mark HarmanUniversity College London
Pre-print
00:45
20m
Paper
Does mutation testing improve testing practices?Technical Track
Technical Track
Goran PetrovićGoogle Inc, Marko IvankovićGoogle Inc, Gordon FraserUniversity of Passau, René JustUniversity of Washington
Pre-print

Information for Participants
Info for Blended Sessions Room 4: