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Trisha Quan

dblp:69/6777 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2008
—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 · 100%
Theoretical computer science
1 paper
Logic in computer science · 100%

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

TopicWeightPapersLastEvidence papers
Debugging and program repair › automated diagnosis
error explanation
0.112008
Error Reporting Logic · ASE 2008
Debugging and program repair
fault localization
0.112008
Error Reporting Logic · ASE 2008
Logic in computer science
first-order logic
0.012008
Error Reporting Logic · ASE 2008

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

user study · 0.2heuristic responsible-object identification · 0.2
YearPublicationVenuePosition
2008 Error Reporting Logic
abstract
When a system fails to meet its specification, it can be difficult to find the source of the error and determine how to fix it. In this paper, we introduce error reporting logic (ERL), an algorithm and tool that produces succinct explanations for why a target system violates a specification expressed in first order predicate logic. ERL analyzes the specification to determine which parts contributed to the failure, and it displays an error message specific to those parts. Additionally, ERL uses a heuristic to determine which object in the target system is responsible for the error. Results from a small user study suggest that the combination of a more focused error message and a responsible object for the error helps users to find the failure in the system more effectively. The study also yielded insights into how the users find and fix errors that may guide future research.
Ciera Jaspan, Trisha Quan, Jonathan Aldrich
ASE2