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Sergio Soares

Researcher at Federal University of Pernambuco

Publications -  115
Citations -  2251

Sergio Soares is an academic researcher from Federal University of Pernambuco. The author has contributed to research in topics: Software development & Aspect-oriented programming. The author has an hindex of 20, co-authored 110 publications receiving 2125 citations. Previous affiliations of Sergio Soares include University of Bari & National Institute of Standards and Technology.

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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.
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Implementing distribution and persistence aspects with aspectJ

TL;DR: This paper reports the experience of using AspectJ, a general-purpose aspect-oriented extension to Java, to implement distribution and persistence aspects in a web-based information system and proposes architecture-specific guidelines that provide practical advice for both restructuring and implementing certain kinds of persistent and distributed applications with Aspect
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.
Journal ArticleDOI

Six years of systematic literature reviews in software engineering: An updated tertiary study

TL;DR: The findings suggest that the software engineering research community is starting to adopt SLRs consistently as a research method, however, the majority of the SLRs did not evaluate the quality of primary studies and fail to provide guidelines for practitioners, thus decreasing their potential impact on software engineering practice.
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Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

TL;DR: The results suggest that prediction models for SNPT might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment in areas with scarce resources.