This program is tentative and subject to change.

Thu 7 Dec 2023 14:00 - 14:15 at Golden Gate C2 - Machine Learning V Chair(s): Prem Devanbu

Executing code is essential for various program analysis tasks, e.g., to detect bugs that manifest through exceptions or to obtain execution traces for further dynamic analysis. However, executing an arbitrary piece of code is often difficult in practice, e.g., because of missing variable definitions, missing user inputs, and missing third-party dependencies. This paper presents LExecutor, a learning-guided approach for executing arbitrary code snippets in an underconstrained way. The key idea is to let a neural model predict missing values that otherwise would cause the program to get stuck, and to inject these values into the execution. For example, LExecutor injects likely values for otherwise undefined variables and likely return values of calls to otherwise missing functions. We evaluate the approach on Python code from popular open-source projects and on code snippets extracted from Stack Overflow. The neural model predicts realistic values with an accuracy between 80.1% and 94.2%, allowing LExecutor to closely mimic real executions. As a result, the approach successfully executes significantly more code than any available technique, such as simply executing the code as-is. For example, executing the open-source code snippets as-is covers only 4.1% of all lines, because the code crashes early on, whereas LExecutor achieves a coverage of 50.1%.

This program is tentative and subject to change.

Thu 7 Dec

Displayed time zone: Pacific Time (US & Canada) change

14:00 - 15:30
Machine Learning VResearch Papers / Ideas, Visions and Reflections / Journal First at Golden Gate C2
Chair(s): Prem Devanbu University of California at Davis
14:00
15m
Talk
LExecutor: Learning-Guided Execution
Research Papers
Beatriz Souza Universit├Ąt Stuttgart, Michael Pradel University of Stuttgart
14:15
15m
Talk
Deeper Notions of Correctness in Image-based DNNs: Lifting Properties from Pixel to Entities
Ideas, Visions and Reflections
Felipe Toledo , David Shriver University of Virginia, Sebastian Elbaum University of Virginia, Matthew B Dwyer University of Virginia
14:30
15m
Talk
Software Architecture Recovery with Information Fusion
Research Papers
Yiran Zhang Nanyang Technological University, Zhengzi Xu Nanyang Technological University, Chengwei Liu Nanyang Technological University, Hongxu Chen Huawei Technologies Co., Ltd., Sun Jianwen Huawei Technologies Co., Ltd, Dong Qiu Huawei Technologies Co., Ltd, Yang Liu Nanyang Technological University
14:45
15m
Talk
What Kinds of Contracts Do ML APIs Need?
Journal First
Samantha Syeda Khairunnesa Bradley University, Shibbir Ahmed Dept. of Computer Science, Iowa State University, Sayem Imtiaz Iowa State University, Hridesh Rajan Dept. of Computer Science, Iowa State University, Gary T. Leavens University of Central Florida
15:00
15m
Talk
Evaluating Transfer Learning for Simplifying GitHub READMEs
Research Papers
Haoyu Gao The University of Melbourne, Christoph Treude University of Melbourne, Mansooreh Zahedi The Univeristy of Melbourne
15:15
15m
Talk
[Remote] CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models
Research Papers
Zhensu Sun Singapore Management University, Xiaoning Du Monash University, Australia, Fu Song State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, and University of Chinese Academy of Sciences Beijing, China, Li Li Beihang University
Pre-print