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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia
Fri 19 May 2023 15:07 - 15:15 at Meeting Room 104 - Software development tools Chair(s): Xing Hu

We present pytest-inline, the first inline testing framework for Python. We recently proposed inline tests to make it easier to test individual program statements, but there is currently no framework-level support available for developers to write inline tests in Python. To fill this gap, we design and implement pytest-inline as a pytest plugin, which is the most popular Python testing framework. In pytest-inline, a developer can write inline tests by assigning test inputs to variables in the target statement and specifying the expected outputs. Then, pytest-inline runs each inline test and fails if the target statement’s output does not match the expected result. In this paper, we describe the design of pytest-inline, the testing features that it provides, and the intended use cases. Our evaluation of pytest- inline on the inline tests we wrote for 80 target statements from 31 open-source Python projects shows that using it to run inline tests incurs negligible overhead, at 0.012x. pytest-inline is open-sourced, and a video demo of pytest-inline can be found at https://www.youtube.com/watch?v=pZgiAxR_uJg.

Fri 19 May

Displayed time zone: Hobart change

13:45 - 15:15
13:45
15m
Talk
Safe low-level code without overhead is practical
Technical Track
Pre-print
14:00
15m
Talk
Sibyl: Improving Software Engineering Tools with SMT SelectionDistinguished Paper Award
Technical Track
Will Leeson University of Virgina, Matthew B Dwyer University of Virginia, Antonio Filieri AWS and Imperial College London
Pre-print
14:15
15m
Talk
Make Your Tools Sparkle with Trust: The PICSE Framework for Trust in Software Tools
SEIP - Software Engineering in Practice
Brittany Johnson George Mason University, Christian Bird Microsoft Research, Denae Ford Microsoft Research, Nicole Forsgren Microsoft Research, Thomas Zimmermann Microsoft Research
Pre-print
14:30
15m
Talk
CoCoSoDa: Effective Contrastive Learning for Code Search
Technical Track
Ensheng Shi Xi'an Jiaotong University, Wenchao Gu The Chinese University of Hong Kong, Yanlin Wang School of Software Engineering, Sun Yat-sen University, Lun Du Microsoft Research Asia, Hongyu Zhang The University of Newcastle, Shi Han Microsoft Research, Dongmei Zhang Microsoft Research, Hongbin Sun Xi'an Jiaotong University
Pre-print
14:45
7m
Talk
Task Context: A Tool for Predicting Code Context Models for Software Development Tasks
DEMO - Demonstrations
Yifeng Wang Zhejiang University, Yuhang Lin Zhejiang University, Zhiyuan Wan Zhejiang University, Xiaohu Yang Zhejiang University
Pre-print Media Attached
14:52
7m
Talk
Continuously Accelerating Research
NIER - New Ideas and Emerging Results
Sergey Mechtaev University College London, Jonathan Bell Northeastern University, Christopher Steven Timperley Carnegie Mellon University, Earl T. Barr University College London, Michael Hilton Carnegie Mellon University
Pre-print
15:00
7m
Talk
An Alternative to Cells for Selective Execution of Data Science Pipelines
NIER - New Ideas and Emerging Results
Lars Reimann University of Bonn, Günter Kniesel-Wünsche University of Bonn
Pre-print
15:07
7m
Talk
pytest-inline: An Inline Testing Tool for Python
DEMO - Demonstrations
Yu Liu University of Texas at Austin, Zachary Thurston Cornell University, Alan Han Cornell University, Pengyu Nie University of Texas at Austin, Milos Gligoric University of Texas at Austin, Owolabi Legunsen Cornell University