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Uirá Kulesza

Researcher at Federal University of Rio Grande do Norte

Publications -  165
Citations -  3519

Uirá Kulesza is an academic researcher from Federal University of Rio Grande do Norte. The author has contributed to research in topics: Software development & Aspect-oriented programming. The author has an hindex of 25, co-authored 155 publications receiving 3222 citations. Previous affiliations of Uirá Kulesza include The Catholic University of America & Pontifical Catholic University of Rio de Janeiro.

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Proceedings ArticleDOI

Evolving software product lines with aspects: an empirical study on design stability

TL;DR: This investigation focused upon a multi-perspective analysis of the evolving product lines in terms of modularity, change propagation, and feature dependency and identified a number of scenarios which positively or negatively affect the architecture stability of aspectual SPLs.
Proceedings ArticleDOI

Modularizing design patterns with aspects: a quantitative study

TL;DR: This paper presents a quantitative study that compares aspect-based and OO solutions for the 23 Gang-of-Four patterns and finds that most aspect-oriented solutions improve separation of pattern-related concerns, although only 4 aspect- oriented implementations have exhibited significant reuse.
Proceedings ArticleDOI

On the impact of aspectual decompositions on design stability: an empirical study

TL;DR: A quantitative case study that evolves a real-life application to assess various facets of design stability of OO and AO implementations and includes an analysis of the application in terms of modularity, change propagation, concern interaction, identification of ripple-effects and adherence to well-known design principles.
Proceedings ArticleDOI

Refactoring product lines

TL;DR: This paper extends the traditional notion of refactoring to an SPL context, and presents a set of sound refactorings for FMs, and evaluates this extendedRefactoring definition for SPL in a real case study in the mobile games domain.
Journal ArticleDOI

A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes

TL;DR: The proposed framework provides a systematic mean for evaluating the data that is generated by a given SZZ implementation and finds that current SZZ implementations still lack mechanisms to accurately identify bug-introducing changes.