Workshop Theme

Software development is going through a paradigm shift, where decision making is increasingly shifting from hand-coded program logic to Deep Learning (DL)--popular applications of Speech Processing, Image Recognition, Robotics, Go game, etc. are using Deep Learning as their core components. Such a wide adoption of DL techniques comes with concerns about the reliability of these systems, especially when DLs are used in safety-critical systems like autonomous cars, medical diagnosis, and aircraft collision avoidance systems. Thus, it has become crucial to rigorously test these DL applications with realistic corner cases to ensure high reliability. Moreover, DLs are also applied in diverse program analysis and software testing techniques resulting in malware detection, fuzz testing, bug-finding, type-checking, etc.; DLs tend to improve the existing testing strategies by learning from past experiences. Therefore, systematically testing Deep Learning applications is an emerging and important Software Engineering problem, especially given their increasing deployment in safety-critical systems.

DeepTest is a workshop targeting research at the intersection of testing and deep learning. This interdisciplinary workshop will explore issues related to:

  1. Deep learning applied to software testing.

  2. Testing applied to Deep Learning applications. 

The workshop will consist of invited talks, presentations based on abstract submissions, and a panel discussion, where we invite all participants to contribute. Although the main focus is on deep learning we encourage submissions that are related more broadly to machine learning and testing and the relationship between the two.

Call for Abstract

We seek 1-page submissions describing work related to the workshop topics. Both preliminary work and new insights in previous work are encouraged. The workshop is meant to be an open forum to foster; we will not have formal proceedings.

This workshop will bring together an international group of researchers in Machine Learning, Testing, and Verification. We invite a range of researchers with both ML and SE backgrounds to come together, discuss their research, establish datasets, tasks, and baselines, and generally help the field build momentum.