Workshop affiliated to , as part of .
The two-day event will feature invited and contributed talks on improving software reliability and developer productivity by using machine learning, including deep learning. The techniques of interest include leveraging big code repositories (such as GitHub) to build models of code and using them for program analysis, synthesis, and repair techniques that advance the state of the art.
Visit the FLoC , select “Workshops only” (unless you also want to attend CAV or other FLoC events), fill in some details, and select two daily tickets for July 18th and July 19th.
The workshop accepts submissions of extended abstracts that will be disseminated on the workshop web site. We welcome applied and theoretical contributions exploring, among others:
The accepted extended abstracts will not preclude subsequent publication in conferences or journals. Submitted papers should be most 5 pages in length (excluding references and appendices) and be prepared using . We will consider original research contributions as well well-prepared surveys and vision statements.
Each accepted extended abstract must be presented by an author at the workshop, 18-19 July 2018.
We have dedicated some budget to cover a limited number of student workshop tickets (and contribute towards travel and accommodation). If you are an MSc student, PhD student or junior Post Doc interested to apply, please contact [email protected] before June 19th with (1) a brief bio, (2) motivations to attend, and a (3) short list of references. We plan to get back to every candidate on June 20th.