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Peiwei Mi

dblp:68/3586 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 1996
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 1Databases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Requirements engineering and software design · 100%
Artificial intelligence
1 paper
Multi-agent systems · 77% Knowledge representation and reasoning · 23%

Topics — the 1 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Requirements engineering and software design
software process
0.011994
The SMART Approach for Software Process Engineering · ICSE 1994

Methods — techniques the papers use, named apart from their topics

knowledge engineering · 0.0SMART approach · 0.0
YearPublicationVenuePosition
1996 A meta-model for formulating knowledge-based models of software development
Peiwei Mi, Walt Scacchi
Decis. Support Syst.1
1994 The SMART Approach for Software Process Engineering
Pankaj K. Garg, Peiwei Mi, Thuan Pham, Walt Scacchi, Gary Thunquest
ICSE2
1990 A Knowledge-Based Environment for Modeling and Simulating Software Engineering Processes
abstract
The design and representation schemes used in constructing a prototype computational environment for modeling and simulating multiagent software engineering processes are described. This environment is called the articulator. An overview of the articulator's architecture identifying five principal components is provided. Three of the components, the knowledge metamodel, the software process behavior simulator, and a knowledge base querying mechanism, are detailed and examples are included. The conclusion reiterates what is unique to this approach in applying knowledge engineering techniques to the problems of understanding the statics and dynamics of complex software engineering processes.>
Peiwei Mi, Walt Scacchi
IEEE Trans. Knowl. Data Eng.1