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Monali Pande

dblp:256/6221 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Software engineering, systems software and programming languages · 1

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
Debugging and program repair · 44% Program analysis · 44% Concurrent programming · 13%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Debugging and program repair
fault localization
0.412019
Visual Analytics for Concurrent Java Executions · ASE 2019
Program analysis › dynamic analysis › trace analysis
trace visualization
0.412019
Visual Analytics for Concurrent Java Executions · ASE 2019
Concurrent programming
concurrency bugs
0.112019
Visual Analytics for Concurrent Java Executions · ASE 2019

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

zooming · 0.8trace filtering · 0.8
YearPublicationVenuePosition
2019 Visual Analytics for Concurrent Java Executions
abstract
Analyzing executions of concurrent software is very difficult. Even if a trace is available, such traces are very hard to read and interpret. A textual trace contains a lot of data, most of which is not relevant to the issue at hand. Past visualization attempts either do not show concurrent behavior, or result in a view that is overwhelming for the user. We provide a visual analytics tool, VA4JVM, for error traces produced by either the Java Virtual Machine, or by Java Pathfinder. Its key features are a layout that spatially associates events with threads, a zoom function, and the ability to filter event data in various ways. We show in examples how filtering and zooming in can highlight a problem without having to read lengthy textual data.
Cyrille Artho, Monali Pande, Qiyi Tang 0001
ASE2