The International Workshop on Programming Language Processing (PLP 2021)

In conjunction with ACM KDD 2021 Conference.

Virtual, August 14th – 18th

Website: http://plpworkshop.com/



Programming language origins in natural language. Different from natural language that is used by humans amongst themselves, programming languages allow humans to tell machines what to do. The meaningful identifier names and natural language documentation allow other developers to understand the author’s intent and then maintain and extend the code. At the same time, the substantial information contained in the code enables the intervention of machine learning algorithms in a variety of software engineering tasks. However, the mining of programming languages could not exactly follow the manner of natural language processing, because of their difference. Programming languages need a high degree of expertise, completeness and precision because computer cannot think outside the statement while natural language may be informal and allow minor errors. The programming language syntax is also not based on natural language grammar. We have witnessed an increasing number of successful machine learning techniques for natural language processing, e.g., GPT (Generative Pre- Training) by Open AI, and BERT (Bidirectional Encoder Representations from Transformers) for language understanding. In this deep learning era, what are the challenges and opportunities to deploy such NLP breakthroughs in programming language processing? What is the current more specialised model for programming language processing? How do machine learning and software engineering researchers apply the knowledge in collaboration to further the field and improve intelligence of the code? We propose to invite world-leading experts from both machine learning and software engineering to discuss and debate the path forward for mining the value of programming languages.



===Topics of interest===

This workshop will provide a premium platform for researchers from both academia and industry to exchange ideas on opportunities, challenges, and cutting-edge techniques of machine learning for software engineering applications and systems. Papers will be accepted under the topics including, but not limited to, the following three broad categories:

Novel Machine Learning Techniques for Programming Language

Weakly supervised machine learning for programming languages
Pretrained models for programming languages
Deep generative models for programming languages
Graph convolutional neural networks for programming languages
Sequence modelling for programming languages
Machine translation for programming languages

Novel Machine Learning Applications to Software Engineering Problems

Deployment of languages to different platforms
Code generation, optimization, and synthesis
Software language validation
Compilation and interpretation techniques
Software language design and implementation
Testing techniques for languages
Simulation techniques for languages

Novel Machine Learning Systems of Software Engineering Tasks

Code recommendation systems
Dialogue and Interactive Systems
Performance benchmarks
User studies evaluating usability
Programming tools, including refactoring editors, checkers, compilers and debuggers
Techniques in secure, parallel, distributed, embedded or mobile environments


=== Submission requirements===

Submissions are strongly recommended to be no more than 4 pages, excluding references or supplementary materials (all in a single pdf). The appropriateness of using additional pages over the recommended length will be judged by reviewers. Papers must be submitted in PDF format to easychair https://easychair.org/conferences/?conf=plp2021 and formatted according to the new Standard ACM Conference Proceedings Template. Concurrent submissions to other journals and conferences are acceptable.



=== Important dates ===

Workshop paper submissions: May 20th, 2021

Workshop paper notification: June 10th, 2021

All deadlines are 11.59 pm UTC -12h ("Anywhere on Earth").



=== Organizers ===

Chang Xu, University of Sydney, Australia

Siqi Ma, University of Queensland, Australia

David Lo, Singapore Management University