Tianpei Xia

College of Engineering

Works (6)

Updated: July 5th, 2023 14:19

2022 article

Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue

2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 144–155.

By: R. Shu n, T. Xia n, L. Williams n & T. Menzies n 

author keywords: Security Vulnerability Prediction; Class Imbalance; Hyperparameter Optimization; Generative Adversarial Networks
TL;DR: The use of optimized GANs are suggested as an alternative method for security vulnerability data class imbalanced issues and further help build better prediction models with resampled datasets. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: September 19, 2022

2022 article

Methods for Stabilizing Models Across Large Samples of Projects (with case studies on Predicting Defect and Project Health)

2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 566–578.

By: S. Majumder n, T. Xia n, R. Krishna  n & T. Menzies n 

author keywords: Defect Prediction; Project Health; Bellwether; Hierarchical Clustering; Random Forest; Two Phase Transfer Learning; Transfer Learning
TL;DR: This paper provides a promising result showing such stable models can be generated using a new transfer learning framework called STABILIZER, and these case studies are the largest demonstration of the generalizability of quantitative predictions of project quality yet reported in the SE literature. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: September 19, 2022

2022 journal article

Omni: automated ensemble with unexpected models against adversarial evasion attack

EMPIRICAL SOFTWARE ENGINEERING, 27(1).

By: R. Shu n, T. Xia n, L. Williams n & T. Menzies n 

author keywords: Hyperparameter optimization; Ensemble defense; Adversarial evasion attack
TL;DR: Omni is a promising approach as a defense strategy against adversarial attacks when compared with other baseline treatments, and it is suggested to create ensemble with unexpected models that are distant from the attacker’s expected model through methods such as hyperparameter optimization. (via Semantic Scholar)
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16. Peace, Justice and Strong Institutions (OpenAlex)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: December 6, 2021

2022 journal article

Predicting health indicators for open source projects (using hyperparameter optimization)

EMPIRICAL SOFTWARE ENGINEERING, 27(6).

By: T. Xia n, W. Fu n, R. Shu n, R. Agrawal  n & T. Menzies n 

author keywords: Hyperparameter optimization; Project health; Machine learning
TL;DR: This is the largest study yet conducted, using recent data for predicting multiple health indicators of open-source projects, and finds that traditional estimation algorithms make many mistakes. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: July 5, 2022

2022 journal article

Sequential Model Optimization for Software Effort Estimation

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 48(6), 1994–2009.

By: T. Xia n, R. Shu n, X. Shen n  & T. Menzies n 

author keywords: Estimation; Software; Tools; Optimization; Data models; Task analysis; Mathematical model; Effort estimation; COCOMO; hyperparameter tuning; regression trees; sequential model optimization
TL;DR: This paper applies a configuration technique called “ROME” (Rapid Optimizing Methods for Estimation), which uses sequential model-based optimization (SMO) to find what configuration settings of effort estimation techniques work best for a particular data set. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: June 15, 2022

2021 journal article

How to Better Distinguish Security Bug Reports (Using Dual Hyperparameter Optimization)

EMPIRICAL SOFTWARE ENGINEERING, 26(3).

By: R. Shu n, T. Xia n, J. Chen n, L. Williams n & T. Menzies n 

author keywords: Hyperparameter Optimization; Data pre-processing; Security bug report
TL;DR: The SWIFT’s dual optimization of both pre-processor and learner is more useful than optimizing each of them individually, and this approach can quickly optimize models that achieve better recalls than the prior state-of-the-art. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: May 3, 2021

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