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ICSE 2020
Wed 24 June - Thu 16 July 2020
Fri 10 Jul 2020 16:05 - 16:11 at Silla - A24-Testing and Debugging 4 Chair(s): Yijun Yu

The increasing use of machine-learning (ML) enabled systems incritical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles. In traditional system development, model-based techniques have been widely adopted, where the central premise is that abstract models of the required system provide a sound basis for judging its implementation. We posit an analogous approach for ML systems using an ML technique that extracts from the high-dimensional training data implicitly describing the required system, a low-dimensional underlying structure—a manifold. It is then harnessed for a range of quality assurance tasks such as test adequacy measurement, test input generation, and runtime monitoring of the target ML system.The approach is built on variational autoencoders, an unsupervised method for learning a pair of mutually near-inverse functions between a given high-dimensional dataset and a low-dimensional representation. Preliminary experiments establish that the proposedmanifold-based approach, for test adequacy drives diversity in testdata, for test generation yields fault-revealing yet realistic test casesand for run-time monitoring provides an independent means to assess trustability of the target system’s output.

Fri 10 Jul
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16:05 - 17:05
A24-Testing and Debugging 4Technical Papers / New Ideas and Emerging Results / Journal First / Demonstrations at Silla
Chair(s): Yijun YuThe Open University, UK
16:05
6m
Talk
Manifold for Machine Learning AssuranceNIER
New Ideas and Emerging Results
Taejoon ByunUniversity of Minnesota, Sanjai RayadurgamUniversity of Minnesota
16:11
12m
Talk
On Learning Meaningful Assert Statements for Unit Test CasesTechnical
Technical Papers
Cody WatsonWashington and Lee University, Michele TufanoMicrosoft, Kevin MoranWilliam & Mary/George Mason University, Gabriele BavotaUniversità della Svizzera italiana, Denys PoshyvanykWilliam and Mary
Pre-print Media Attached
16:23
12m
Talk
TRADER: Trace Divergence Analysis and Embedding Regulation for Debugging Recurrent Neural NetworksArtifact ReusableTechnicalArtifact Available
Technical Papers
Guanhong TaoPurdue University, Shiqing MaRutgers University, Yingqi LiuPurdue University, USA, Qiuling XuPurdue University, Xiangyu ZhangPurdue University
Pre-print
16:35
3m
Talk
DeepMutation: A Neural Mutation ToolDemo
Demonstrations
Michele TufanoMicrosoft, Jason KimkoWilliam & Mary, Shiya WangWilliam & Mary, Cody WatsonWashington and Lee University, Gabriele BavotaUniversità della Svizzera italiana, Massimiliano Di PentaUniversity of Sannio, Denys PoshyvanykWilliam and Mary
Pre-print
16:38
8m
Talk
Specification Patterns for Robotic MissionsJ1
Journal First
Claudio MenghiUniversity of Luxembourg, Christos TsigkanosTU Vienna, Patrizio PelliccioneUniversity of L'Aquila and Chalmers | University of Gothenburg, Carlo GhezziPolitecnico di Milano, Thorsten BergerChalmers | University of Gothenburg
16:46
8m
Talk
ProXray: Protocol Model Learning and Guided Firmware AnalysisJ1
Journal First
Farhaan FowzeUniversity of Florida, Dave (Jing) TianPurdue University, Grant HernandezUniversity of Florida, Kevin ButlerUniv. Florida, Tuba YavuzUniversity of Florida
16:54
6m
Talk
Visual Sketching: From Image Sketches to CodeNIER
New Ideas and Emerging Results
Marcelo d'AmorimFederal University of Pernambuco, Rui AbreuInstituto Superior Técnico, U. Lisboa & INESC-ID, Carlos MelloFederal University of Pernambuco
Pre-print Media Attached