J
Jiang Zheng
Researcher at North Carolina State University
Publications - 21
Citations - 570
Jiang Zheng is an academic researcher from North Carolina State University. The author has contributed to research in topics: Regression testing & Source code. The author has an hindex of 10, co-authored 21 publications receiving 537 citations. Previous affiliations of Jiang Zheng include ABB Ltd.
Papers
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Journal ArticleDOI
On the value of static analysis for fault detection in software
TL;DR: In this article, the authors analyzed static analysis faults and test and customer-reported failures for three large-scale industrial software systems developed at Nortel Networks and found that automated static analysis is effective at identifying assignment and checking faults, allowing the later software production phases to focus on more complex, functional, and algorithmic faults.
Proceedings ArticleDOI
Applying regression test selection for COTS-based applications
TL;DR: This paper presents the application of the lightweight Integrated - Black-box Approach for Component Change Identification (I-BACCI) Version 3 process that select regression tests for applications that use COTS components.
Proceedings ArticleDOI
An initial study of predictive machine learning analytics on large volumes of historical data for power system applications
Jiang Zheng,Aldo Dagnino +1 more
TL;DR: The results indicated the feasibility of forecasting substations fault events and power load using machine learning algorithm written in MapReduce paradigm or machine learning tools specific for Big Data.
Proceedings ArticleDOI
Regression Test Selection for Black-box Dynamic Link Library Components
TL;DR: The Integrated - Black-box Approach for Component Change Identification (I-BACCI) process that selects regression tests for applications based upon static binary code analysis to Version 4 to support DLL components is evolved.
Proceedings ArticleDOI
An initial study of a lightweight process for change identification and regression test selection when source code is not available
TL;DR: A lightweight integrated-black-box approach for component change identification (I-BACCI) process for selection of regression tests for user/glue code that uses COTS components that can reduce the required regression tests by 40% on average.