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Vidya Priyadarshini Narayanan

dblp:24/10607 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—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 testing · 67% Programming languages and type systems · 33%

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

TopicWeightPapersLastEvidence papers
Software testing › test generation
constraint-based test generation
0.112011
Mixed constraints for test input generation - An initial exploration · ASE 2011
Programming languages and type systems
specification language
0.112011
Mixed constraints for test input generation - An initial exploration · ASE 2011
Software testing
test input generation
0.112011
Mixed constraints for test input generation - An initial exploration · ASE 2011

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

constraint solving · 0.1
YearPublicationVenuePosition
2011 Mixed constraints for test input generation - An initial exploration
abstract
The use of specifications provides an effective technique to automate testing. A form of specification that automates generation of test inputs is logical constraints that define properties of desired inputs. Recent advances in constraint solving technology have made the use of constraints particularly attractive. However, manually writing constraints to define complex inputs to real-world programs can pose a significant burden on the user and restrict their wider use. We envision a novel approach to facilitate the use of constraints: to provide a mixed notation for writing the properties. Our key insight is that different properties can lend to easier formulation using different programming paradigms. Thus, a notation that supports more than one paradigm, e.g., declarative and imperative paradigms, can enable achieving a sweet-spot in minimizing the manual effort required in constraint formulation. Moreover, solving such constraints is also likely to be more efficient as different properties may require different paradigms for more direct and accurate representation. This paper presents our vision and gives an illustration to make a case for the usefulness of such a notation.
Shadi Abdul Khalek, Vidya Priyadarshini Narayanan, Sarfraz Khurshid
ASE2