Shenghui (Samuel) Gu ☕️

Shenghui (Samuel) Gu

(he/him)

Postdoctoral Researcher

Nanda Laboratory | EECS | University of Ottawa

Professional Summary

Postdoctoral Researcher at the University of Ottawa specializing in Trustworthy AI and Software Engineering, holding a Ph.D. from Nanjing University. Research lies at the intersection of AI Safety and System Reliability, with deep expertise spanning LLM-driven testing, search-based software engineering for autonomous systems, and AIOps for distributed architectures. Dedicated to developing rigorous, interpretable, and scalable methodologies that leverage generative AI to solve complex validation challenges in safety-critical and large-scale industrial systems.

Education

Ph.D. in Software Engineering

2017-09-01
2023-06-30

Nanjing University

B.Eng. in Software Engineering

2013-09-01
2017-06-30

Nanjing University

Interests

Automated software testing and analysis Artificial Intelligence Trustworthy AI LLMs AIOps Software Log Analytics DevOps Empirical Software Engineering
Recent Publications
Using Cooperative Co-evolutionary Search to Generate Metamorphic Test Cases for Autonomous Driving Systems featured image

Using Cooperative Co-evolutionary Search to Generate Metamorphic Test Cases for Autonomous Driving Systems

Autonomous Driving Systems (ADSs) rely on Deep Neural Networks, allowing vehicles to navigate complex, open environments. However, the unpredictability of these scenarios …

hossein-yousefizadeh
How Do Developers' Profiles and Experiences Influence their Logging Practices? An Empirical Study of Industrial Practitioners featured image

How Do Developers' Profiles and Experiences Influence their Logging Practices? An Empirical Study of Industrial Practitioners

Logs record the behavioral data of running programs and are typically generated by executing log statements. Software developers generally carry out logging practices with clear …

guoping-rong
TrinityRCL: Multi-Granular and Code-Level Root Cause Localization Using Multiple Types of Telemetry Data in Microservice Systems featured image

TrinityRCL: Multi-Granular and Code-Level Root Cause Localization Using Multiple Types of Telemetry Data in Microservice Systems

The microservice architecture has been commonly adopted by large scale software systems exemplified by a wide range of online services. Service monitoring through anomaly detection …

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Shenghui (Samuel) Gu
Logging Practices in Software Engineering: A Systematic Mapping Study featured image

Logging Practices in Software Engineering: A Systematic Mapping Study

_Background:_ Logging practices provide the ability to record valuable runtime information of software systems to support operations tasks such as service monitoring and …

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Shenghui (Samuel) Gu
Can You Capture Information As You Intend To? A Case Study on Logging Practice in Industry featured image

Can You Capture Information As You Intend To? A Case Study on Logging Practice in Industry

_Background:_ Logs provide crucial information to understand the dynamic behavior of software systems in modern software development and maintenance. Usually, logs are produced by …

guoping-rong
Locating Anomaly Clues for Atypical Anomalous Services: An Industrial Exploration featured image

Locating Anomaly Clues for Atypical Anomalous Services: An Industrial Exploration

Continuity and steadiness are vital for services with massive users, which requires the anomalies of services should be detected and resolved in a timely manner. Our previous work …

guoping-rong
Recent & Upcoming Talks
How Do Developers' Profiles and Experiences Influence their Logging Practices? An Empirical Study of Industrial Practitioners featured image

How Do Developers' Profiles and Experiences Influence their Logging Practices? An Empirical Study of Industrial Practitioners

An empirical study on how developers' profiles and experience influence their logging practices and intentions.

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Shenghui (Samuel) Gu
模型可解释性 featured image

模型可解释性

简单介绍模型的可解释性。

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Shenghui (Samuel) Gu
软件著作权申请介绍 featured image

软件著作权申请介绍

简单介绍如何申请软件著作权。

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Shenghui (Samuel) Gu
DevOpsEnvy: An Education Support System for DevOps featured image

DevOpsEnvy: An Education Support System for DevOps

A web-based system that helps manage and monitor student DevOps projects, easing evaluation for teachers and supporting team collaboration.

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Shenghui (Samuel) Gu
Recent Posts

AIOps 简介

AIOps 是人工智能在软件运维中的应用,即利用机器学习、大数据和自动化决策来完成一系列需要人为进行大量手工干预的传统运维操作流程。 通过对运维数据的算法分析,AIOps 能够帮助运维或 DevOps 团队更智能、更快速的完成运维工作,从而在业务运营和客户受到影响之前,更早的发现软件系统问题并快速解决。 在 DevOps 环境下,运维团队能够通过 AIOps 应对现代 IT 环境产生的大量复杂数据,从而防止中断,维持正常运行时间,实现持续的服务保障。 AIOps 已经成为监控和管理混合、动态、分布式和组件化的现代 IT 环境的关键。 本节将从软件运维的发展历史切入,介绍 AIOps 的必要性、构成及工作方式,并简述了 AIOps 的优势和使用场景。

从 Hexo 到 Hugo

最近又开始折腾博客了,大概拖了有一个月了才开始记录迁移博客的感受。 简言之,让我从 Hexo 迁移到 Hugo 最大的原因是 Emacs Org mode 下的 Hugo 插件。