Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Drew Schleck

dblp:120/2020 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2012
—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 · 77% Empirical software engineering · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering › mining software repositories
bug report analysis
0.012012
Reusing debugging knowledge via trace-based bug search · OOPSLA 2012

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

hadoop-based query engine · 0.1execution traces · 0.1bug query language · 0.1
YearPublicationVenuePosition
2012 Reusing debugging knowledge via trace-based bug search
abstract
Some bugs, among the millions that exist, are similar to each other. One bug-fixing tactic is to search for similar bugs that have been reported and resolved in the past. A fix for a similar bug can help a developer understand a bug, or even directly fix it. Studying bugs with similar symptoms, programmers may determine how to detect or resolve them. To speed debugging, we advocate the systematic capture and reuse of debugging knowledge, much of which is currently wasted. The core challenge here is how to search for similar bugs. To tackle this problem, we exploit semantic bug information in the form of execution traces, which precisely capture bug semantics. This paper introduces novel tool and language support for semantically querying and analyzing bugs. We describe OSCILLOSCOPE, an Eclipse plugin, that uses a bug trace to exhaustively search its database for similar bugs and return their bug reports. OSCILLOSCOPE displays the traces of the bugs it returns against the trace of the target bug, so a developer can visually examine the quality of the matches. OSCILLOSCOPE rests on our bug query language (BQL), a flexible query language over traces. To realize OSCILLOSCOPE, we developed an open infrastructure that consists of a trace collection engine, BQL, a Hadoop-based query engine for BQL, a trace-indexed bug database, as well as a web-based frontend. OSCILLOSCOPE records and uploads bug traces to its infrastructure; it does so automatically when a JUnit test fails. We evaluated OSCILLOSCOPE on bugs collected from popular open-source projects. We show that OSCILLOSCOPE accurately and efficiently finds similar bugs, some of which could have been immediately used to fix open bugs.
Zhongxian Gu, Earl T. Barr, Drew Schleck, Zhendong Su 0001
OOPSLA3