Demo Data from EDBT 2026 / reviewers in the wild / expert
Peiwei Mi
dblp:68/3586
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Requirements engineering and software design
software process |
0.0 | 1 | 1994 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 |
ICSE | 2 |
| 1990 | A Knowledge-Based Environment for Modeling and Simulating Software Engineering ProcessesabstractThe 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 |