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Yulissa Arroyo-Paredes

dblp:207/6556 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—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 · 44% Program analysis · 44% Requirements engineering and software design · 13%

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

TopicWeightPapersLastEvidence papers
Software testing
fault detection
0.312017
Detecting argument selection defects · Proc. ACM Program. Lang. 2017
Program analysis
static analysis
0.312017
Detecting argument selection defects · Proc. ACM Program. Lang. 2017
Requirements engineering and software design › interface design
API design
0.112017
Detecting argument selection defects · Proc. ACM Program. Lang. 2017

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

statistical threshold tuning · 0.3identifier name analysis · 0.3
YearPublicationVenuePosition
2017 Detecting argument selection defects
abstract
Identifier names are often used by developers to convey additional information about the meaning of a program over and above the semantics of the programming language itself. We present an algorithm that uses this information to detect argument selection defects, in which the programmer has chosen the wrong argument to a method call in Java programs. We evaluate our algorithm at Google on 200 million lines of internal code and 10 million lines of predominantly open-source external code and find defects even in large, mature projects such as OpenJDK, ASM, and the MySQL JDBC. The precision and recall of the algorithm vary depending on a sensitivity threshold. Higher thresholds increase precision, giving a true positive rate of 85%, reporting 459 true positives and 78 false positives. Lower thresholds increase recall but lower the true positive rate, reporting 2,060 true positives and 1,207 false positives. We show that this is an order of magnitude improvement on previous approaches. By analyzing the defects found, we are able to quantify best practice advice for API design and show that the probability of an argument selection defect increases markedly when methods have more than five arguments.
Andrew C. Rice, Edward Aftandilian, Ciera Jaspan, Emily Johnston, Michael Pradel, Yulissa Arroyo-Paredes
Proc. ACM Program. Lang.6