TCSE logo 
 Sigsoft logo
Sustainability badge
Fri 2 May 2025 11:30 - 11:45 at 214 - AI for Testing and QA 5 Chair(s): Chunyang Chen

Maintaining a “green” mainline branch—where all builds pass successfully—is crucial but challenging in fast-paced, large-scale software development environments, particularly with concurrent code changes in large monorepos. SubmitQueue, a system designed to address these challenges, speculatively executes builds and only lands changes with successful outcomes. However, despite its effectiveness, the system faces inefficiencies in resource utilization, leading to a high rate of premature build aborts and delays in landing smaller changes blocked by larger conflicting ones.

This paper introduces enhancements to SubmitQueue, focusing on optimizing resource usage and improving build prioritization. Central to this is our innovative probabilistic model, which distinguishes between changes with shorter and longer build times to prioritize builds for more efficient scheduling. By leveraging a machine learning model to predict build times and incorporating this into the probabilistic framework, we expedite the landing of smaller changes blocked by conflicting larger time-consuming changes. Additionally, introducing a concept of speculation threshold ensures that only the most likely builds are executed, reducing unnecessary resource consumption.

After implementing these enhancements across Uber’s major monorepos (Go, iOS, and Android), we observed a reduction in Continuous Integration (CI) resource usage by approximately 53%, CPU usage by 44%, and P95 waiting times by 37%. These improvements highlight the enhanced efficiency of SubmitQueue in managing large-scale software changes while maintaining a green mainline.

Fri 2 May

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

11:00 - 12:30
AI for Testing and QA 5SE In Practice (SEIP) at 214
Chair(s): Chunyang Chen TU Munich
11:00
15m
Talk
ASTER: Natural and Multi-language Unit Test Generation with LLMsAward Winner
SE In Practice (SEIP)
Rangeet Pan IBM Research, Myeongsoo Kim Georgia Institute of Technology, Rahul Krishna IBM Research, Raju Pavuluri IBM T.J. Watson Research Center, Saurabh Sinha IBM Research
Pre-print
11:15
15m
Talk
Automated Code Review In Practice
SE In Practice (SEIP)
Umut Cihan Bilkent University, Vahid Haratian Bilkent Univeristy, Arda İçöz Bilkent University, Mert Kaan Gül Beko, Ömercan Devran Beko, Emircan Furkan Bayendur Beko, Baykal Mehmet Ucar Beko, Eray Tüzün Bilkent University
Pre-print
11:30
15m
Talk
CI at Scale: Lean, Green, and Fast
SE In Practice (SEIP)
Dhruva Juloori Uber Technologies, Inc, Zhongpeng Lin Uber Technologies Inc., Matthew Williams Uber Technologies, Inc, Eddy Shin Uber Technologies, Inc, Sonal Mahajan Uber Technologies Inc.
11:45
15m
Talk
Moving Faster and Reducing Risk: Using LLMs in Release DeploymentAward Winner
SE In Practice (SEIP)
Rui Abreu Meta, Vijayaraghavan Murali Meta Platforms Inc., Peter C Rigby Meta / Concordia University, Chandra Sekhar Maddila Meta Platforms, Inc., Weiyan Sun Meta Platforms, Inc., Jun Ge Meta Platforms, Inc., Kaavya Chinniah Meta Platforms, Inc., Audris Mockus University of Tennessee, Megh Mehta Meta Platforms, Inc., Nachiappan Nagappan Meta Platforms, Inc.
12:00
15m
Talk
Prioritizing Large-scale Natural Language Test Cases at OPPO
SE In Practice (SEIP)
Haoran Xu , Chen Zhi Zhejiang University, Tianyu Xiang Guangdong Oppo Mobile Telecommunications Corp., Ltd., Zixuan Wu Zhejiang University, Gaorong Zhang Zhejiang University, Xinkui Zhao Zhejiang University, Jianwei Yin Zhejiang University, Shuiguang Deng Zhejiang University; Alibaba-Zhejiang University Joint Institute of Frontier Technologies
12:15
15m
Talk
Search+LLM-based Testing for ARM SimulatorsArtifact-AvailableArtifact-FunctionalArtifact-Reusable
SE In Practice (SEIP)
Bobby Bruce University of California at Davis, USA, Aidan Dakhama King's College London, Karine Even-Mendoza King’s College London, William B. Langdon University College London, Hector Menendez King’s College London, Justyna Petke University College London
:
:
:
: