Data Mining and Knowledge Discovery - Call for Papers: Special Issue on Programming Language Processing
Call for Papers: Special Issue on Programming Language Processing
Guest Editors
Chang Xu, The University of Sydney, Australia (c.xu@sydney.edu.au)
Siqi Ma, The University of Queensland, Australia (slivia.ma@uq.edu.au)
David Lo, Singapore Management University, Singapore (davidlo@smu.edu.sg)
Background, Motivation, Topics
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? This special issue is to invite world-leading experts from data mining, machine learning and software engineering to discuss and debate the path forward for mining the value of programming languages.
The list of possible topics includes, but is not limited to:
Novel Data Mining 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 Data Mining 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 Data Mining 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 Guidelines
The submission must describe high-quality, original research. By submitting a manuscript, authors acknowledge that it has not been previously published or accepted for publication in a substantially similar form in any peer-reviewed venue including journal and conference.
Information for Authors can be found at: https://www.springer.com/journal/10618/submission-guidelines (this opens in a new tab).
Submit manuscripts to:
http://DAMI.edmgr.com (this opens in a new tab).
Indicate the special issue "S.I. Programming Language Processing" in the "Additional Information Step".
Important Dates:
Submission deadline: November 30, 2021
Reviewing round 1 ends: January 2022
Revisions: February 2022
Final Acceptance: March 2022