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2nd International Workshop on Machine Learning techniques for Programming Languages

Welcome to ML4PL, a workshop on machine learning techniques applied to programming language-related research and development. 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.

Accepted Talks

Title
Buffer Overflow Detection for C Programs is Hard to Learn
ML4PL
Can We Learn Some PL Theory? How To Make Use of a Corpus of Subtype Checks
ML4PL
Detecting anomalies in Kotlin code
ML4PL
Generating Software Adaptations using Machine Learning
ML4PL
Inferring Input Structure for Machine LearningKeynote
ML4PL
On the Importance of Common Sense in Program Synthesis
ML4PL
Subtype Polymorphism à la carte via Machine Learning on Dependent Types
ML4PL

Call for Submissions

Over the last 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

Submissions should take the form of talk abstract or 2-page problem statements. Materials of accepted talks will be published in ACM DL.

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Wed 18 Jul

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11:00 - 12:30
Session #1ML4PL at Bangkok
Chair(s): Hila Peleg Technion, Israel, Artem Pelenitsyn Czech Technical University in Prague
11:00
60m
Talk
Inferring Input Structure for Machine LearningKeynote
ML4PL
Andreas Zeller Saarland University
12:00
30m
Talk
On the Importance of Common Sense in Program Synthesis
ML4PL
Hila Peleg Technion, Israel
14:00 - 15:30
Session #2ML4PL at Bangkok
Chair(s): Artem Pelenitsyn Czech Technical University in Prague
14:00
30m
Talk
Buffer Overflow Detection for C Programs is Hard to Learn
ML4PL
Cristina Cifuentes Oracle Labs, Yang Zhao Oracle Labs, Xingzhong Du Oracle Labs, Paddy Krishnan
14:30
30m
Talk
Generating Software Adaptations using Machine Learning
ML4PL
Nicolás Cardozo Universidad de los Andes, Ivana Dusparic Trinity College Dublin, Ireland
15:00
30m
Talk
Detecting anomalies in Kotlin code
ML4PL
Timofey Bryksin , Victor Petukhov ITMO University, Kirill Smirenko Saint Petersburg State University, Nikita Povarov JetBrains
16:00 - 18:00
Session #3ML4PL at Bangkok
Chair(s): Hila Peleg Technion, Israel
16:00
30m
Talk
Subtype Polymorphism à la carte via Machine Learning on Dependent Types
ML4PL
Jerry Swan University of York, Colin Johnson University of Kent, Edwin Brady University of St. Andrews, UK
16:30
30m
Talk
Can We Learn Some PL Theory? How To Make Use of a Corpus of Subtype Checks
ML4PL
Artem Pelenitsyn Czech Technical University in Prague
17:00
30m
Meeting
Open Forum
ML4PL