ECOOP 2015
Sun 5 - Fri 10 July 2015 Prague, Czech Republic

Welcome to ML4PL, the first workshop on machine learning techniques applied to programming language-related applications. This workshop puts an emphasis on identifying open problem rather than presenting solution, and encourages discussion amongst the participants. Attendance will be limited to ensure that meeting retains an interactive character.

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Tue 7 Jul
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10:00 - 10:15: WelcomeML4PL at Moravia III
10:00 - 10:15
Day opening
Welcome and introductions
10:15 - 11:00: Invited TalkML4PL at Moravia III
10:15 - 11:00
Machine Learning for Programming
Martin VechevETH Zurich
11:00 - 12:30: Session 1ML4PL at Moravia III
11:00 - 11:30
Learning to Decipher the Heap
Marc BrockschmidtMicrosoft Research
11:30 - 12:00
PPAML: Probabilistic Programming Advancing Machine Learning
12:00 - 12:30
Man vs. Machine: Challenges of Integrating Programming Languages and People
Emery D. BergerUniversity of Massachusetts, Amherst
13:45 - 15:45: Session 2ML4PL at Moravia III
13:45 - 14:15
Problems and opportunities — Program similarity
Eran YahavTechnion
14:15 - 14:45
Inferring Coding Conventions with Machine Learning
Miltiadis AllamanisUniversity of Edinburgh, Earl T. BarrUniversity College London, Christian BirdMicrosoft Research, Charles SuttonUniversity of Edinburgh
14:45 - 15:15
Using topic models to understand programming languages literature
Kathleen FisherTufts University
15:15 - 15:45
Scaling Program Synthesis by Exploiting Existing Code
James BornholtUniversity of Washington, Emina TorlakUniversity of Washington
16:10 - 18:10: Session 3ML4PL at Moravia III
16:10 - 16:40
Problems and opportunities – Statistical modeling in (declarative) PLs
Molham ArefLogicblox
16:40 - 17:10
Bimodal Modelling of Source Code and Natural Language
Andrew D. GordonMicrosoft Research and University of Edinburgh
17:10 - 17:40
Machine learning for predictive modeling and recommender systems automation
Pavel KordikCzech Technical University in Prague

Call for Papers

Over the last few years, we have seen a rapid growth in the use of machine-learning technologies in programming languages and systems. This growth is driven by the need to design programming languages to analyze, detect patterns, and make sense of Big Data, along with the increasing complexity of programming language tools, including analyzers and compilers, and computer architectures. The scale of complexity in available unstructured data and system tools has reached a stage where simple heuristics and solutions are no longer feasible or do not deliver adequate performance. At the same time, statistical and machine learning techniques have become more mainstream.

This workshop is a broad forum to bring together researchers with interests in the intersection of programming languages and system tools with machine learning.

Topics of interest include (but are not limited to):

  • Program analysis + machine learning
  • Programming languages + machine learning
  • Compiler optimizations + machine learning
  • Computer architecture + machine learning
  • Probabilistic programming languages
  • Design space exploration

The workshop will feature a couple of longer talks, and the short problem statements.

Submissions should take the form of talk abstract or 2 page problem statements.