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Bao Nguyen

Bao Nguyen

Bao N. Nguyen is a Senior Software Engineer at Google. He is working on Google-wide Continuous Integration and Continuous Deployment systems shared by 30K+ engineers and running 40M+ test targets continuously. Prior to Google, he worked at VMware in the Platform UI team. He is in the organizing committee of several industry and academic conferences including GTAC (2018) and ICST (2017, 2019). He is also serving in PC for a number of international conferences and journals on software engineering. He obtained his PhD in Computer Science from University of Maryland, College Park on exploratory model-based testing. His current research interests include software automation, CI/CD, software testing, distributed systems, cloud computing and machine learning.

Research homepage: https://sites.google.com/corp/view/baonn

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    Taming Google-Scale Continuous Testing
    Atif Memon
    Bao Nguyen
    Eric Nickell
    John Micco
    Sanjeev Dhanda
    Rob Siemborski
    Zebao Gao
    ICSE '17:Proceedings of the 39th International Conference on Software Engineering (2017) (to appear)
    Preview abstract Growth in Google’s code size and feature churn rate has seen increased reliance on continuous integration (CI) and testing to maintain quality. Even with enormous resources dedicated to testing, we are unable to regression test each code change individually, resulting in increased lag time between code check-ins and test result feedback to developers. We report results of a project that aims to reduce this time by: (1) controlling test workload without compromising quality, and (2) distilling test results data to inform developers, while they write code, of the impact of their latest changes on quality. We model, empirically understand, and leverage the correlations that exist between our code, test cases, developers, programming languages, and code-change and test-execution frequencies, to improve our CI and development processes. Our findings show: very few of our tests ever fail, but those that do are generally “closer” to the code they test; certain frequently modified code and certain users/tools cause more breakages; and code recently modified by multiple developers (more than 3) breaks more often. NOTE: You can find the anonymized dataset for our paper on Google drive: https://drive.google.com/open?id=0B5_QHWCtac81VGNKYnhrQkJBZGM View details
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