Demo Data from EDBT 2026 / reviewers in the wild / expert
Nija Shi
dblp:71/3696
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 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
1 paper |
Software maintenance and evolution · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › reverse engineering
design pattern detection |
0.1 | 1 | 2006 | Reverse Engineering of Design Patterns from Java Source Code · ASE 2006 |
Software maintenance and evolution
program comprehension |
0.1 | 1 | 2006 | Reverse Engineering of Design Patterns from Java Source Code · ASE 2006 |
Methods — techniques the papers use, named apart from their topics
static program analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | IKOS: A Framework for Static Analysis Based on Abstract Interpretation
Guillaume Brat, Jorge A. Navas, Nija Shi, Arnaud Venet |
SEFM | 3 |
| 2006 | Reverse Engineering of Design Patterns from Java Source CodeabstractRecovering design patterns can enhance existing source code analysis tools by bringing program understanding to the design level. This paper presents a new, fully automated pattern detection approach. The new approach is based on our reclassification of the GoF patterns by their pattern intent. We argue that the GoF pattern catalog classifies design patterns in the forward-engineering sense; our reclassification is better suited for reverse engineering. Our approach uses lightweight static program analysis techniques to capture program intent. This paper also describes our tool, PINOT, that implements this new approach. PINOT detects all the GoF patterns that have concrete definitions driven by code structure or system behavior. Our tool is faster, more accurate, and targets more patterns than existing pattern detection tools. PINOT has been used successfully in detecting patterns in Java AWT, JHotDraw, Swing, Apache Ant, and many other programs and packages Nija Shi, Ronald A. Olsson |
ASE | 1 |