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Alexandru Bodea

dblp:325/0899 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021

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 · 87% Software maintenance and evolution · 13%

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

TopicWeightPapersLastEvidence papers
Software testing
test smell detection
0.612022
Pytest-Smell: a smell detection tool for Python unit tests · ISSTA 2022
Software testing
unit testing
0.612022
Pytest-Smell: a smell detection tool for Python unit tests · ISSTA 2022

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

static analysis · 0.6
YearPublicationVenuePosition
2022 Pytest-Smell: a smell detection tool for Python unit tests
abstract
Code quality and design are key factors in building a successful software application. It is known that a good internal structure assures a good external quality. To improve code quality, several guidelines and best practices are defined. Along with these, a key contribution is brought by unit testing. Just like the source code, unit test code is subject to bad programming practices, known as defects or smells, that have a negative impact on the quality of the software system. As a consequence, the system becomes harder to understand, maintain, and more prone to issues and bugs. In this respect, methods and tools that automate the detection of the aforementioned unit test smells are of the utmost importance.
Alexandru Bodea
ISSTA1