ML4SE: Machine Learning for Software Engineering |
Website | https://ml4se.github.io |
Submission link | https://easychair.org/conferences/?conf=ml4se |
Submission deadline | April 5, 2019 |
Software has become an essential part of everyday life, and its development is producing enormous amounts of data. This includes not only source code, but also related artifacts such as change histories, test results, other execution behavior, bug reports, code reviews, and communications between developers. This constitutes an amazing wealth of rich and detailed information. At the same time, machine learning is flourishing, with a range of powerful new technologies achieving practical successes in many domains.
This workshop will bring together researchers interested in the intersection of software engineering and machine learning research. There is great promise in solving and assisting with software authoring and maintenance tasks such as coding, testing, debugging, porting, bug-patching, refactoring, optimizing, etc. Machine learning gives new tools for addressing software engineering research challenges, and software engineering challenges motivate new machine learning research. In the workshop we will discuss recent advances in this area, what challenges remain, and share ideas for how to continue progressing forward.
Invited Speakers
We are delighted that the workshop will feature two invited speakers:
- Cindy Rubio González (University of California at Davis)
- Mayur Naik (University of Pennsylvania)
Call for papers
This interdisciplinary workshop will explore issues related to the application of machine learning techniques to software engineering tasks, including topics such as:
- Code suggestion in IDEs (modelling "naturalness" of code)
- Mining programming idioms
- Statistical inference of types and other annotations
- Software bug and "smell" identification
- Automated program repair
- Program synthesis (e.g. superoptimization or from natural language)
- Code summarization, code retrieval, code annotation
- Automatically identifying relevant coding collaborators
- New applications of machine learning to source code and related artifacts
We invite short position papers or early-stage research papers of at most 4 pages in length. Several submissions will be invited for presentation.
We invite contributions of up to four pages in length describing early-stage research or position papers. Submissions that are posted on arxiv or under review elsewhere are welcome. Short versions of recently published submissions are also welcome, but priority will be given to work that hasn't previously been presented at a similar workshop. Please submit through EasyChair.
Registration
Space is limited, so pre-registration is required. There is no registration fee. Slots will be reserved for the program committee and authors of accepted papers. We welcome participation from others, but we may need to cap registration if we exceed space constraints. If you would like to attend, please fill out the registration request form. However, please note that registration is not confirmed until you receive an email from the organizers confirming space availability (the form submission confirmation is not sufficient).
Important Dates
- April 5, 2019: Paper submission deadline (AoE)
- April 19, 2019: Author notification
- June 1, 2019: Workshop
Organizing committee
- Marc Brockschmidt, Microsoft Research
- Satish Chandra, Facebook
- Prem Devanbu, University of California, Davis
- Baishakhi Ray, Columbia University
- Daniel Tarlow, Google Brain
Venue
The conference will be held at the Google Offices in Montreal.
Contact
For questions or comments about the workshop, please contact Marc Brockschmidt, Satish Chandra,Premkumar Devanbu, Baishakhi Ray, or Danny Tarlow.