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Yogesh Padmanaban

dblp:129/8243 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—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
Software maintenance and evolution · 100%

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

TopicWeightPapersLastEvidence papers
Software maintenance and evolution › software documentation
API documentation
0.212013
Inferring likely mappings between APIs · ICSE 2013
Software maintenance and evolution
software ecosystems
0.212013
Inferring likely mappings between APIs · ICSE 2013
Software maintenance and evolution › software reengineering › software modernization › software migration
software porting
0.212013
Inferring likely mappings between APIs · ICSE 2013

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

knowledge harvesting from applications · 0.2
YearPublicationVenuePosition
2013 Inferring likely mappings between APIs
abstract
Software developers often need to port applications written for a source platform to a target platform. In doing so, a key task is to replace an application's use of methods from the source platform API with corresponding methods from the target platform API. However, this task is challenging because developers must manually identify mappings between methods in the source and target APIs, e.g., using API documentation. We develop a novel approach to the problem of inferring mappings between the APIs of a source and target platform. Our approach is tailored to the case where the source and target platform each have independently-developed applications that implement similar functionality. We observe that in building these applications, developers exercised knowledge of the corresponding APIs. We develop a technique to systematically harvest this knowledge and infer likely mappings between the APIs of the source and target platform. The output of our approach is a ranked list of target API methods or method sequences that likely map to each source API method or method sequence. We have implemented this approach in a prototype tool called Rosetta, and have applied it to infer likely mappings between the Java2 Platform Mobile Edition and Android graphics APIs.
Amruta Gokhale, Vinod Ganapathy, Yogesh Padmanaban
ICSE3