Sushmita Azad
Sushmita Azad is a software engineer at Google DeepMind working on training reliability and efficiency infrastructure for the Gemini foundation models. At Alphabet, she applies machine learning techniques to large-scale infrastructure challenges. She previously led major initiatives for Google’s Test Automation Platform (TAP), working on predictive modeling for continuous integration pipelines and multi-architecture testing. Sush received her MS in Computer Science from the University of Illinois, Urbana-Champaign in 2020, where she applied NLP and ML techniques to computer science education.
Authored Publications
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Taming the Variants Multi-Architecture Continuous Testing at Google
Chandrakanth Chittappa
Ali Esmaeeli
Laura Macaddino
Sam Manfreda
David Margolin
Dharma Naidu
Sabuj Pattanayek
Sachin Sable
Ruslan Sakevych
Dushyant Acharya
Adrian Berding
Kevin Crossan
Wolff Dobson
Abhay Singh
19th IEEE International Conference on Software Testing, Verification and Validation (ICST) 2026, Daejeon, Republic of Korea, IEEE
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Enterprises are increasingly adopting multiple general-purpose computer architectures in the data center. This leads to new testing challenges as it creates demand to qualify the software for the additional architectures. Naively double-testing all software for both architectures is costly and unnecessary. Further, reconfiguring CI/CD to take advantage of the new architecture can be non-trivial at scale. This paper introduces CI/CD variants and an optimized testing cycle to solve these twin challenges. We empirically evaluate our solution's impact on human and machine expenses using 44k projects at Google on real production data. First, we estimate saving ~25% of machine expenses at the negligible cost of a few delayed breakage detections per day. Second, we estimate a 90+% reduction in human cost for migrating the configuration. All features described in this paper are now Generally Available at Google and we report this as an empirical case study in scaling CI/CD to new architectures.
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SafeRevert: When Can Breaking Changes be Automatically Reverted?
Eric Nickell
2024 IEEE Conference on Software Testing, Verification and Validation (ICST), IEEE, Toronto, ON, Canada
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When bugs or defects are introduced into a large scale software repository, they reduce productivity. Programmers working on related areas of the code will encounter test failures, compile breakages, or other anomalous behavior. On encounter- ing these issues, they will need to troubleshoot and determine that their changes were not the cause of the error and that another change is a fault. They must then find that change and revert it to return the repository to a healthy state. In the past, our group has identified ways to identify the root cause (or culprit) change that introduced a test failure even when the test is flaky. This paper focuses on a related issue: At what point does the Continuous Integration system have enough evidence to support automatically reverting a change? We will motivate the problem, provide several methods to address it, and empirically evaluate our solution on a large set (34,000) real world breaking changes that occurred at Google.
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