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Mon 14 - Fri 18 November 2022 Singapore
Wed 16 Nov 2022 14:00 - 15:30 at SRC Auditorium 2 - Invited Tutorial - Zhi Jin Chair(s): Domenico Bianculli

Programming languages are artificial and highly restricted languages. But source code is there to tell computers as well as programmers what to do, as an act of communication. Despite its weird syntax and is riddled with different delimiters, the good news is that the very large corpus of open-source code is available. That makes it reasonable to apply machine learning techniques to source code to enable the source code analytics.

Despite there are plenty of deep learning frameworks in the field of NLP, source code analytics has different features. In addition to the conventional way of coding, understanding the meaning of code involves many perspectives. The source code representation could be the token sequence, the API call sequence, the data dependency graph, and the control flow graph, as well as the program hierarchy, etc. This tutorial will tell the long, ongoing, and fruitful journey on exploiting the potential power of deep learning techniques in source code analytics. It will highlight that how code representation models can be utilized to support software engineers to perform different tasks that require proficient programming knowledge. The exploratory work show that code does imply the learnable knowledge, more precisely the learnable tacit knowledge. Although such knowledge is not easily transferrable between humans, it can be transferred between the automated programming tasks. A vision for future research will be stated for source code analytics.

Wed 16 Nov

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

14:00 - 15:30
Invited Tutorial - Zhi JinPlenary Events at SRC Auditorium 2
Chair(s): Domenico Bianculli University of Luxembourg
Multi-perspective representation learning for source code analytics
Plenary Events
Zhi Jin Peking University