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Mateus C. Lucena

dblp:311/8789 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
0009-0001-2865-3930ORCID · reported

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

Software engineering, systems software and programming languages · 1 · 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 maintenance and evolution · 56% Empirical software engineering · 44%

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

TopicWeightPapersLastEvidence papers
Software maintenance and evolution › release engineering
continuous integration
0.512021
Automatically Deciding on the Integration of Commits Based on Their Descriptions · ASE 2021
Empirical software engineering
mining software repositories
0.512021
Automatically Deciding on the Integration of Commits Based on Their Descriptions · ASE 2021
Software maintenance and evolution › software configuration management › software release management
software release
0.112021
Automatically Deciding on the Integration of Commits Based on Their Descriptions · ASE 2021

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

natural language processing · 0.5
YearPublicationVenuePosition
2021 Automatically Deciding on the Integration of Commits Based on Their Descriptions
abstract
Continuous Integration is a critical problem for software maintenance in global projects, compromising companies’ performance, which tends to accumulate a high-resolution time due to the approval process, conflict resolution, tests, and validations. The process of the validation involves the commit description interpretation and can be automated by NLP-mechanisms. This paper presents an intelligent NLP-based approach to evaluate whether the commits can be integrated into a certain software release based only on their descriptions. Our experiments showed an accuracy of 92.9%.
Samuel C. Fonseca, Mateus C. Lucena, Tiago M. Reis, Pedro F. Cabral, Walmir A. Silva, Flávia de Souza Santos, Felipe T. Giuntini, Juliano Sales
ASE2