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Richard L. Piazza

dblp:20/2787 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1997
—ORCID · none

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

Artificial intelligence and machine learning · 1Software 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
Software maintenance and evolution · 100%
Artificial intelligence
1 paper
Planning, search and constraint satisfaction · 100%

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

TopicWeightPapersLastEvidence papers
Software maintenance and evolution › reverse engineering
design recovery
0.011997
Design Recovery for Distributed Systems · IEEE Trans. Software Eng. 1997
Software maintenance and evolution
reverse engineering
0.011997
Design Recovery for Distributed Systems · IEEE Trans. Software Eng. 1997
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
search control
0.011989
Approximating Learned Search Control Knowledge · ML 1989

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

task flow extraction · 0.0design knowledge · 0.0approximation · 0.0
YearPublicationVenuePosition
1997 Design Recovery for Distributed Systems
abstract
Two factors limit the utility of reverse engineering technology for many distributed software systems. First, with the exception of tools that support Ada and its explicit tasking constructs, reverse engineering tools fail to capture information concerning the flow of information between tasks. Second, relatively few reverse engineering tools are available for programming languages in which many older legacy applications were written (e.g., Jovial, CMS-2, and various assembly languages). We describe approaches that were developed for overcoming these limitations. In particular, we have implemented an approach for automatically extracting task flow information from a command and control system written in CMS-2. Our approach takes advantage of a small amount of externally provided design knowledge in order to recover design information relevant to the distributed nature of the target system.
Lester J. Holtzblatt, Richard L. Piazza, Howard B. Reubenstein, Susan N. Roberts, David R. Harris 0001
IEEE Trans. Software Eng.2
1989 Approximating Learned Search Control Knowledge
Melissa P. Chase, Monte Zweben, Richard L. Piazza, John D. Burger, Paul P. Maglio, Haym Hirsh
ML3