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 MayDisplayed time zone: Hobart change
13:45 - 15:15 | Software development toolsDEMO - Demonstrations / Technical Track / SEIP - Software Engineering in Practice / NIER - New Ideas and Emerging Results at Meeting Room 104 Chair(s): Xing Hu Zhejiang University | ||
13:45 15mTalk | Safe low-level code without overhead is practical Technical Track Pre-print | ||
14:00 15mTalk | Sibyl: Improving Software Engineering Tools with SMT Selection Technical Track Will Leeson University of Virgina, Matthew B Dwyer University of Virginia, Antonio Filieri AWS and Imperial College London Pre-print | ||
14:15 15mTalk | 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 15mTalk | 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 7mTalk | 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 7mTalk | 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 7mTalk | An Alternative to Cells for Selective Execution of Data Science Pipelines NIER - New Ideas and Emerging Results Pre-print | ||
15:07 7mTalk | 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 |