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Yagnik Suchak

dblp:202/8430 · 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
Empirical software engineering · 87% Software maintenance and evolution · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering › mining software repositories › commit analysis
commit classification
0.312017
GitcProc: a tool for processing and classifying GitHub commits · ISSTA 2017
Empirical software engineering
mining software repositories
0.312017
GitcProc: a tool for processing and classifying GitHub commits · ISSTA 2017

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

regular expression matching · 0.3
YearPublicationVenuePosition
2017 GitcProc: a tool for processing and classifying GitHub commits
abstract
Sites such as GitHub have created a vast collection of software artifacts that researchers interested in understanding and improving software systems can use. Current tools for processing such GitHub data tend to target project metadata and avoid source code processing, or process source code in a manner that requires significant effort for each language supported. This paper presents GitcProc, a lightweight tool based on regular expressions and source code blocks, which downloads projects and extracts their project history, including fine-grained source code information and development time bug fixes. GitcProc can track changes to both single-line and block source code structures and associate these changes to the surrounding function context with minimal set up required from users. We demonstrate GitcProc's ability to capture changes in multiple languages by evaluating it on C, C++, Java, and Python projects, and show it finds bug fixes and the context of source code changes effectively with few false positives.
Casey Casalnuovo, Yagnik Suchak, Baishakhi Ray, Cindy Rubio-González
ISSTA2