Workshop on Deep Learning and Software Engineering

This award supports a workshop to explore synergies between Deep Learning and Software Engineering. The goal is to accelerate research that uses Deep Learning in research and practice to improve techniques and tools for Software Engineering through the power of Deep Learning. Conversely, deep-learning based systems, which are emerging in many application domains, need new Software Engineering approaches render them correct, reliable and comprehensible. The workshop will bring together researchers and practitioners in both fields to discuss research priorities community resources needed to accelerate research in the intersection of Deep Learning and Software Engineering.

Deep Learning represents a fundamental shift in the manner by which machines learn patterns from data by automatically extracting salient features for a given computational task, as opposed to relying upon human intuition. Deep Learning approaches are characterized by architectures comprised of several layers that perform mathematical transformations, according to sets of learnable parameters, on data passing through them. These computational layers and parameters form models that can be trained for specific tasks, such as image classification, by updating the parameters according to a model?s performance on a labeled set of training data. Given the immense amount of data in software repositories that can serve as training data, deep learning techniques have ushered in advancements across a range of tasks in software engineering research including automatic software repair, code suggestion, defect prediction, malware detection, feature location, and many others. The workshop will review the state of the research and practice and give guidance to the community about opportunities and challenges.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.