Carlos Diego Nascimento Damasceno

Carlos Diego Nascimento Damasceno
Radboud University | RU · Institute for Computing and Information Sciences

PhD
Postdoctoral Researcher @ Radboud Uni

About

30
Publications
4,350
Reads
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65
Citations
Citations since 2016
26 Research Items
65 Citations
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Introduction
Carlos Diego Nascimento Damasceno earned his PhD in Computer Science from the Institute of Mathematics and Computer Sciences at the University of São Paulo (ICMC-USP). Carlos Diego does research in Software Engineering and Model-Based Testing. His current research projects are focusing on Model-Driven Engineering and Open Science
Additional affiliations
November 2018 - December 2020
University of Leicester
Position
  • Visiting Research Student
Description
  • This visit aims at (1) studying the theory of software product lines (SPL) and developing model-based testing (MBT) techniques for them and (2) designing automata learning algorithms to automatically build models of SPLs from concrete products.
May 2016 - July 2020
University of São Paulo
Position
  • PhD Student
Description
  • Earned his PhD from the University of São Paulo (USP) Member of the Software Engineering Laboratory (LabES) at USP/ICMC. (CAPES scholarship)
March 2014 - February 2016
University of São Paulo
Position
  • Master's Student
Description
  • Master in Science at the program of computer science and mathematical computing. Member of the Software Engineering Laboratory (LabES) at USP/ICMC. (CNPq scholarship)
Education
April 2020 - December 2021
University of São Paulo
Field of study
  • Project Management
February 2016 - July 2020
University of São Paulo
Field of study
  • Computer Science and Computational Mathematics
February 2014 - May 2016
University of São Paulo
Field of study
  • Computer Science and Computational Mathematics

Publications

Publications (30)
Conference Paper
Substantial effort has been spent on extending specification notations and their associated reasoning techniques to software product lines (SPLs). Family-based analysis techniques operate on a single artifact, referred to as a family model, that is annotated with variability constraints. This modeling approach paves the way for efficient model-base...
Article
Full-text available
Context - Access control systems demand rigorous verification and validation approaches, otherwise, they can end up with security breaches. Finite state machines based testing has been successfully applied to RBAC systems and enabled to obtain effective test cases, but very expensive. To deal with the cost of these test suites, test prioritization...
Conference Paper
Full-text available
Access control mechanisms demand rigorous software testing approaches, otherwise they can end up with security flaws. Finite state machines (FSM) have been used for testing Role-Based Access Control (RBAC) mechanisms and complete, but significantly large, test suites can be obtained. Experimental studies have shown that recent FSM testing methods c...
Preprint
Full-text available
Thousands of vulnerabilities are reported on a monthly basis to security repositories, such as the National Vulnerability Database. Among these vulnerabilities, software misconfiguration is one of the top 10 security risks for web applications. With this large influx of vulnerability reports, software fingerprinting has become a highly desired capa...
Chapter
Full-text available
Thousands of vulnerabilities are reported on a monthly basis to security repositories, such as the National Vulnerability Database. Among these vulnerabilities, software misconfiguration is one of the top 10 security risks for web applications. With this large influx of vulnerability reports, software fingerprinting has become a highly desired capa...
Preprint
Behavioral models enable the analysis of the functionality of software product lines (SPL), e.g., model checking and model-based testing. Model learning aims at constructing behavioral models for software systems in some form of a finite state machine. Due to the commonalities among the products of an SPL, it is possible to reuse the previously lea...
Chapter
The chapter addresses how Brazilian diplomacy has articulated the promotion of scientific and technological innovation in collaboration with foreign countries. The authors compare the Brazilian mechanisms with reference countries, such as Switzerland and India, and identify the Brazilians' strengths and weaknesses. Finally, they suggest a list of c...
Preprint
Full-text available
Behavioral models are the key enablers for behavioral analysis of Software Product Lines (SPL), including testing and model checking. Active model learning comes to the rescue when family behavioral models are non-existent or outdated. A key challenge on active model learning is to detect commonalities and variability efficiently and combine them i...
Article
Full-text available
Para o efetivo desenvolvimento de políticas educacionais, de inclusão e permanência é necessário ter ferramentas e métodos adequados para analisar os dados coletados. Assim, este artigo apresenta uma nova ferramenta para apoiar análises dos microdados do Enade utilizando técnicas de mineração de dados. Esta ferramenta foi desenvolvida durante um es...
Conference Paper
Full-text available
Research artifact sharing is known to strengthen the transparency of scientific studies. However, in the lack of common discipline-specific guidelines for artifacts evaluation, subjective and conflicting expectations may happen and threaten artifact quality. In this paper, we discuss our preliminary ideas for a framework based on quality management...
Preprint
Research artifact sharing is known to strengthen the transparency of scientific studies. However, in the lack of common discipline-specific guidelines for artifacts evaluation, subjective and conflicting expectations may happen and threaten artifact quality. In this paper, we discuss our preliminary ideas for a framework based on quality management...
Preprint
Sharing research artifacts is known to help people to build upon existing knowledge, adopt novel contributions in practice, and increase the chances of papers receiving attention. In Model-Driven Engineering (MDE), openly providing research artifacts plays a key role, even more so as the community targets a broader use of AI techniques, which can o...
Preprint
Full-text available
The Knowledge Discovery in Database (KDD) process permits the detection of pattern in databases, where this analysis may be compromised if database is not consistent, making necessary the use of data cleaning techniques. This paper presents a tool based in similarity functions to help the preprocessing of databases and it behaved efficiently in the...
Preprint
Full-text available
The next-generation sequencers such as Illumina and SOLiD platforms generate a large amount of data, commonly above 10 Gigabytes of text files. Particularly, the SOLiD platform allows the sequencing of multiple samples in a single run, called multiplex run, through a tagging system called Barcode. This feature requires a computational process for s...
Article
Full-text available
Family-based behavioral analysis operates on a single specification artifact, referred to as family model, annotated with feature constraints to express behavioral variability in terms of conditional states and transitions. Family-based behavioral modeling paves the way for efficient model-based analysis of software product lines. Family-based beha...
Thesis
Maintenance and evolution have been accepted as integral principles in the software development life-cycle. They are essential for any system that operates in or addresses problems or activities of the real world if it is to remain useful and profitable. Nevertheless, as time passes and modifications occur, modeling artifacts are often neglected du...
Chapter
Full-text available
Software systems undergo several changes along their life-cycle and hence, their models may become outdated. To tackle this issue, we propose an efficient algorithm for adaptive learning, called () that improves upon the state of the art by exploring observation tables on-the-fly to discard redundant prefixes and deprecated suffixes. Using 18 versi...
Article
Full-text available
New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, which enables the sequencing of several samples in a single run. It implies in cost reduction and simplifies the analysis of related sa...
Conference Paper
Full-text available
Priorização de testes baseada em similaridade é uma abordagem que usa funções de similaridade para priorizar casos de teste dos pares mais distintos para os mais semelhantes. Neste contexto, o cálculo de matrizes de similaridade desempenha um papel importante, porém caro (O(n2)), pois essas matrizes são base para o processo de priorização. Algoritm...
Thesis
Access Control (AC) is a major pillar in software security. In short, AC ensures that only intended users can access resources and only the required access to accomplish some task will be given. In this context, Role Based Access Control (RBAC) has been established as one of the most important paradigms of access control. In an organization, users...
Conference Paper
Full-text available
Model­ Based Testing (MBT) is a testing approach which uses explicit formal models to specify and automatize test case generation. It has been successfully applied in funcional testing, however there are still challenges in non­functional requirements testing, as security. This paper presents a systematic literature review about model­based securit...
Conference Paper
Full-text available
RESUMO O sequenciadores de nova geração como as plataformas Illumina e SOLiD geram uma grande quantidade de dados, comumente, acima de 10 Gigabytes de arquivos-texto. Particularmente, a plataforma SOLiD permite o sequenciamento de múltiplas amostras em uma única corrida, denominada de corrida multiplex, por meio de um sistema de marcação chamado Ba...
Conference Paper
Full-text available
The Knowledge Discovery in Database (KDD) process permits the detection of pattern in databases, where this analysis may be compromised if database is not consistent, making necessary the use of data cleaning techniques. This paper presents a tool based in similarity functions to help the preprocessing of databases and it behaved efficiently in the...

Questions

Question (1)
Question
Dear authors,
Is there any other paper about mining HAs apart from the one published in 2015? I'm interested in instantiating your framework in the context of automotive systems and I would like to know if there are other works more recent you've published.

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Cited By

Projects

Projects (4)
Archived project
The papers added to this project are results of scientific initiation projects that I enrolled during my undergraduate degree.
Project
Maintenance and evolution have been accepted as integral principles in the software development life-cycle. They are essential for any system that operates in or addresses problems or activities of the real world if it is to remain useful and profitable. Nevertheless, as time passes and modifications occur, modeling artifacts are often neglected due to the lack of proper maintenance. Hence, it may render outdated models and hinder the application of model-based reasoning techniques, such as model-based testing and model checking. To address these issues, recent academic and industrial studies have shown that finite state machine (FSM) model learning techniques are becoming increasingly popular in software verification and testing. Despite these advances, model learning algorithms are still hampered by scalability issues, as well as the constant changes over time that may require learning from scratch. Furthermore, there is a lack of investigations about learning strategies for software product lines (SPL), i.e., systems where variants shall co-exist to satisfying the needs of distinct market segments and hence, incorporate variability in space. In this PhD Thesis, we improve upon the state-of-the-art of model-based software engineering by introducing theoretical and experimental contributions to address model learning in the setting of evolving systems that incorporate modifications over time and variability in space. Our main contributions are three-fold: (i) We have introduced the partial-Dynamic L* M , an adaptive algorithm that explores models from pre-existing versions on- the-fly to discard redundant and deprecated knowledge in terms of input sequences that may not lead to state discovery. Using realistic models of the OpenSSL toolkit, we have shown that our algorithm has been more efficient than state-of-the-art techniques and less sensitive to software evolution. (ii) We have filled the gap of model learning algorithms for variability-intensive systems by introducing the FFSMDiff algorithm. It is an automated technique to identify similar behavior shared among product-specific FSMs, annotate states, and transitions with feature constraints, and integrate them into succinct featured finite state machines (FFSM). Using 105 FSMs derived from six SPLs of academic benchmarks, we have shown that our algorithm can effectively merge families of state machines into succinct FFSMs, especially if there is high feature reuse among products. (iii) We have extended our expertise upon the FFSMDiff algorithm and reported our experiences on learning FFSMs through product sampling. Our results have indicated that FFSMs learned by sampling can be as precise as those learned from exhaustive analysis and hence, collectively cover the behavior of an SPL.