publications

2023

Model-Driven Optimization: Towards Performance-Enhancing Low-Level Encodings

Lars Arragon, Carlos Diego N Damasceno, and Daniel Strüber

In MODELS’23: ACM/IEEE International Conference on Model Driven Engineering Languages and Systems. Workshop on Artifical Intelligence and Model-Driven Engineering, 2023

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An Experimental Evaluation of Conformance Testing Techniques in Active Automata Learning

Bharat Garhewal, and Carlos Diego Nascimento Damasceno

In 2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS), Oct 2023

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Active automata learning is a technique for dynamically learning finite state machine models of black-box systems. Conformance testing is a well-known bottleneck during learning. While multiple conformance testing techniques (CTTs) for Finite State Machines have been proposed, there is a lack of empirical studies that assess the effects of these CTTs \emphduring learning. In this work, we compare the performance of eight different CTTs (W, Wp, HSI, H-ADS, H, SPY, SPY-H, I-ADS) while learning 46 models from different communication protocols. Moreover, we propose \mathttAPFD_L as a metric for characterizing the efficiency of automata learning experiments in terms of fault detection capacity. This metric allows identifying CTTs with a lower total cost regarding the number of symbols and resets and a higher rate of state discovery during learning. Our results indicate that while the total cost entailed by CTTs in learning tends to be negligible, we found a significant difference in fault detection rate in learning. Nevertheless, the differences in fault detection rates become negligible when CTTs are applied in randomized mode. These findings reveal the positive role that randomness can have in improving learning efficiency, despite compromising test completeness.
@inproceedings{garhewal_damasceno_2023_cttlearn, booktitle = {2023 {ACM}/{IEEE} 26th {International} {Conference} on {Model} {Driven} {Engineering} {Languages} and {Systems} ({MODELS})}, author = {Garhewal, Bharat and Damasceno, Carlos Diego Nascimento}, month = oct, year = {2023}, keywords = {TODO}, _pages = {285--296}, title = {An {Experimental} {Evaluation} of {Conformance} {Testing} {Techniques} in {Active} {Automata} {Learning}}, publisher = {{ACM}}, url = {https://doi.org/10.1109/MODELS58315.2023.00012}, doi = {10.1109/MODELS58315.2023.00012}, }

2022

Family-Based Fingerprint Analysis: A Position Paper

Carlos Diego N. Damasceno, and Daniel Strüber

In A Journey from Process Algebra via Timed Automata to Model Learning : Essays Dedicated to Frits Vaandrager on the Occasion of His 60th Birthday, Oct 2022

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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 capability to discover distinctive and efficient signatures and recognize reportedly vulnerable software implementations. Due to the exponential worst-case complexity of fingerprint matching, designing more efficient methods for fingerprinting becomes highly desirable, especially for variability-intensive systems where optional features add another exponential factor to its analysis. This position paper presents our vision of a framework that lifts model learning and family-based analysis principles to software fingerprinting. In this framework, we propose unifying databases of signatures into a featured finite state machine and using presence conditions to specify whether and in which circumstances a given input-output trace is observed. We believe feature-based signatures can aid performance improvements by reducing the size of fingerprints under analysis.
@inproceedings{dd2022_fbfingerprint, author = {Damasceno, Carlos Diego N. and Str{\"u}ber, Daniel}, editor = {Jansen, Nils and Stoelinga, Mari{\"e}lle and van den Bos, Petra}, title = {Family-Based Fingerprint Analysis: A Position Paper}, booktitle = {A Journey from Process Algebra via Timed Automata to Model Learning : Essays Dedicated to Frits Vaandrager on the Occasion of His 60th Birthday}, year = {2022}, publisher = {Springer Nature Switzerland}, address = {Cham}, pages = {137--150}, isbn = {978-3-031-15629-8}, doi = {10.1007/978-3-031-15629-8_8}, url = {https://doi.org/10.1007/978-3-031-15629-8_8}, }
A tool for analysing higher-order feature interactions in preprocessor annotations in C and C++ projects

David Korsman, Carlos Diego N. Damasceno, and Daniel Strüber

In Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume B, Sep 2022

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Feature interactions are an intricate phenomenon: they can add value to software systems, but also lead to subtle bugs and complex, emergent behavior. Having a clearer understanding of feature interactions in practice can help practitioners to select appropriate quality assurance techniques for their systems and researchers to guide further research efforts. In this paper, we present pdparser, a Python-based tool for analysing structural feature interactions in software systems developed with C and C++ preprocessor. Our tool relies on a lightweight methodology to quantify the frequency of pairwise and higher-order feature interactions and the percentage of code affected by them. We showcase the individual characteristics brought forward by the automated analysis of one toy example and two open-source text editors: Vim and Emacs. The source code and a demo video are available on GitHub at https://github.com/dkorsman/pdparser.
@inproceedings{korsman2022:splc:demoandtool:pdparser, address = {New York, NY, USA}, series = {{SPLC} '22}, title = {A tool for analysing higher-order feature interactions in preprocessor annotations in {C} and {C}++ projects}, copyright = {All rights reserved}, isbn = {978-1-4503-9206-8}, url = {https://doi.org/10.1145/3503229.3547027}, doi = {10.1145/3503229.3547027}, urldate = {2022-09-14}, booktitle = {Proceedings of the 26th {ACM} {International} {Systems} and {Software} {Product} {Line} {Conference} - {Volume} {B}}, publisher = {Association for Computing Machinery}, author = {Korsman, David and Damasceno, Carlos Diego N. and Strüber, Daniel}, month = sep, year = {2022}, keywords = {feature interaction, preprocessors, product lines, static analysis}, pages = {70--73}, }

A Lightweight Approach for Model Checking Variability-Based Graph Transformations

Mitchell Albers, Carlos Diego Nascimento Damasceno, and Daniel Strüber

In 2022 13th International Workshop on Graph Computation Models (GCM), Sep 2022

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@inproceedings{albers2022:gcm:mc:vb:gt,
  author = {Albers, Mitchell and Damasceno, Carlos Diego Nascimento and Strüber, Daniel},
  booktitle = {2022 13th International Workshop on Graph Computation Models (GCM)},
  title = {A Lightweight Approach for Model Checking Variability-Based Graph Transformations},
  year = {2022},
  series = {GCM '22},
}

Model-Driven Optimization: Generating Smart Mutation Operators for Multi-Objective Problems

Niels Harten, Carlos Diego Nascimento Damasceno, and Daniel Strüber

In 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Sep 2022

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@inproceedings{vanHarten2022:seaa:mdo:smart:mutation:op,
  author = {van Harten, Niels and Damasceno, Carlos Diego Nascimento and Strüber, Daniel},
  booktitle = {2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)},
  title = {Model-Driven Optimization: Generating Smart Mutation Operators for Multi-Objective Problems},
  year = {2022},
  _doi = {10.1109/CLOUD.2019.00016},
  _url = {https://doi.ieeecomputersociety.org/10.1109/CLOUD.2019.00016},
  publisher = {IEEE Computer Society},
  series = {SEAA '22},
}

Adaptive Behavioral Model Learning for Software Product Lines

Shaghayegh Tavassoli, Carlos Diego N. Damasceno, Ramtin Khosravi, and 1 more author

In Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A, Sep 2022

(Best Paper Award - Research Track)

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Behavioral models enable the analysis of the functionality of software product lines (SPL), e.g., model checking and model-based testing. Model learning aims to construct behavioral models. Due to the commonalities among the products of an SPL, it is possible to reuse the previously-learned models during the model learning process. In this paper, an adaptive approach, called PL*, for learning the product models of an SPL is presented based on the well-known L* algorithm. In this method, after learning each product, the sequences in the final observation table are stored in a repository which is used to initialize the observation table of the remaining products. The proposed algorithm is evaluated on two open-source SPLs and the learning cost is measured in terms of the number of rounds, resets, and input symbols. The results show that for complex SPLs, the total learning cost of PL* is significantly lower than that of the non-adaptive method in terms of all three metrics. Furthermore, it is observed that the order of learning products affects the efficiency of PL*. We introduce a heuristic to determine an ordering which reduces the total cost of adaptive learning.
@inproceedings{tavassoli2022:splc:adaptive_learning, author = {Tavassoli, Shaghayegh and Damasceno, Carlos Diego N. and Khosravi, Ramtin and Mousavi, Mohammad Reza}, title = {Adaptive Behavioral Model Learning for Software Product Lines}, year = {2022}, isbn = {9781450394437}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3546932.3546991}, doi = {10.1145/3546932.3546991}, booktitle = {Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A}, pages = {142–153}, numpages = {12}, keywords = {software product lines, automata learning, adaptive model learning, finite state machines}, location = {Graz, Austria}, note = {(Best Paper Award - Research Track)}, series = {SPLC '22}, }
A Benchmark for Active Learning of Variability-Intensive Systems

Shaghayegh Tavassoli, Carlos Diego N. Damasceno, Mohammad Reza Mousavi, and 1 more author

In Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A, Sep 2022

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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 into concise family models. Benchmarks and their associated metrics will play a key role in shaping the research agenda in this promising field and provide an effective means for comparing and identifying relative strengths and weaknesses in the forthcoming techniques. In this challenge, we seek benchmarks to evaluate the efficiency (e.g., learning time and memory footprint) and effectiveness (e.g., conciseness and accuracy of family models) of active model learning methods in the software product line context. These benchmark sets must contain the structural and behavioral variability models of at least one SPL. Each SPL in a benchmark must contain products that requires more than one round of model learning with respect to the basic active learning L* algorithm. Alternatively, tools supporting the synthesis of artificial benchmark models are also welcome.
@inproceedings{tavassoli2022:splc:challenge, author = {Tavassoli, Shaghayegh and Damasceno, Carlos Diego N. and Mousavi, Mohammad Reza and Khosravi, Ramtin}, title = {A Benchmark for Active Learning of Variability-Intensive Systems}, year = {2022}, isbn = {9781450394437}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3546932.3547014}, doi = {10.1145/3546932.3547014}, booktitle = {Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A}, pages = {245–249}, numpages = {5}, keywords = {featured finite state machines, behavioral variability, model learning, benchmarking}, location = {Graz, Austria}, series = {SPLC '22}, }

2021

The role of Brazil’s Ministry of Foreign Affairs in promoting Innovation Diplomacy

Augusto Costa, Carlos Diego Nascimento Damasceno, Celio Melo, and 10 more authors

In Innovation Diplomacy: Brazil´s and Regional´s Analysis and Roadmap, Sep 2022
Mineração de dados do Enade de 2016 a 2018: uma análise sobre o município de Araçatuba

Mayk F. Choji, Carlos Diego Nascimento Damasceno, Ig Ibert Bittencourt, and 1 more author

RENOTE, Sep 2021

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Para o efetivo desenvolvimento de políticas educacionais, de inclusao e permanencia é necessário ter ferramentas e métodos adequados para analisar os dados coletados. Assim, este artigo apresenta uma nova ferramenta para apoiar analises dos microdados do Enade utilizando técnicas de mineração de dados. Esta ferramenta foi desenvolvida durante um estudo de caso sobre o perfil socioeconômico dos concluintes de graduacão do município de Araçatuba/SP, baseado nos microdados de 2016 a 2018. Como resultado, foram extraídas algumas regras de associação como, por exemplo, alunos brancos de IES privadas que escolheram o curso visando inserção no mercado de trabalho, tendem a ter baixas notas no Enade; enquanto alunos autodeclarados pretos, pardos ou indígenas que escolheram o curso pelo mesmo motivo apresentaram melhores notas.
@article{choji_mineracao_2021, title = {Mineração de dados do {Enade} de 2016 a 2018: uma análise sobre o município de {Araçatuba}}, volume = {19}, shorttitle = {Mineração de dados do {Enade} de 2016 a 2018}, language = {pt}, number = {2}, journal = {RENOTE}, author = {Choji, Mayk F. and Damasceno, Carlos Diego Nascimento and Bittencourt, Ig Ibert and Isotani, Seiji}, year = {2021}, keywords = {elemento-jogo, ensino de literatura, gamificação}, }
Quality Guidelines for Research Artifacts in Model-Driven Engineering

Carlos Diego Nascimento Damasceno, and Daniel Strüber

In 2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), Oct 2021

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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 only become feasible if large open datasets and confidence measures for their quality are available. However, the current lack of common discipline-specific guidelines for research data sharing opens the opportunity for misunderstandings about the true potential of research artifacts and subjective expectations regarding artifact quality. To address this issue, we introduce a set of guidelines for artifact sharing specifically tailored to MDE research. To design this guidelines set, we systematically analyzed general-purpose artifact sharing practices of major computer science venues and tailored them to the MDE domain. Subsequently, we conducted an online survey with 90 researchers and practitioners with expertise in MDE. We investigated our participants’ experiences in developing and sharing artifacts in MDE research and the challenges encountered while doing so. We then asked them to prioritize each of our guidelines as essential, desirable, or unnecessary. Finally, we asked them to evaluate our guidelines with respect to clarity, completeness, and relevance. In each of these dimensions, our guidelines were assessed positively by more than 92% of the participants. To foster the reproducibility and reusability of our results, we make the full set of generated artifacts available in an open repository at https://mdeartifacts.github.io/.
@inproceedings{damascenostrueber2021_qa_mde, booktitle = {2021 {ACM}/{IEEE} 24th {International} {Conference} on {Model} {Driven} {Engineering} {Languages} and {Systems} ({MODELS})}, author = {Damasceno, Carlos Diego Nascimento and Strüber, Daniel}, month = oct, year = {2021}, keywords = {Model driven engineering, Model-Driven Engineering, Software, Computer Science - Software Engineering, Computer science, Guidelines, Open Science, Artificial intelligence, Current measurement, Quality Management, Reproducibility of results, Software artifacts}, pages = {285--296}, title = {Quality {Guidelines} for {Research} {Artifacts} in {Model}-{Driven} {Engineering}}, publisher = {{ACM}}, url = {https://doi.org/10.1109/MODELS50736.2021.00036}, doi = {10.1109/MODELS50736.2021.00036}, }
Towards Multi-Criteria Prioritization of Best Practices in Research Artifact Sharing

Carlos Diego Nascimento Damasceno, Isotilia Costa Melo, and Daniel Strüber

In Anais do Workshop de Práticas de Ciência Aberta para Engenharia de Software (OpenScienSE), Sep 2021

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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 principles (5W2H) that can aid in the establishment of common guidelines for artifact evaluation and sharing. Also, using the Analytic Hierarchy Process, we discuss how research communities could join efforts to aid the guidelines’ adequacy to research priorities. These combined methodologies constitute a novelty for software engineering research which can foster research software sustainability.
@inproceedings{damascenostrueber2021_opensciense, title = {Towards {Multi}-{Criteria} {Prioritization} of {Best} {Practices} in {Research} {Artifact} {Sharing}}, copyright = {Copyright (c)}, url = {https://sol.sbc.org.br/index.php/opensciense/article/view/17137}, doi = {10.5753/opensciense.2021.17137}, language = {en}, urldate = {2021-10-02}, booktitle = {Anais do {Workshop} de {Práticas} de {Ciência} {Aberta} para {Engenharia} de {Software} ({OpenScienSE})}, publisher = {SBC}, author = {Damasceno, Carlos Diego Nascimento and Melo, Isotilia Costa and Strüber, Daniel}, month = sep, year = {2021}, pages = {1--6}, }
Learning by sampling: learning behavioral family models from software product lines

Carlos Diego Nascimento Damasceno, Mohammad Reza Mousavi, and Adenilso da Silva Simao

Empirical Software Engineering, Jan 2021

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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 behavioral model learning incorporates feature model analysis and model learning principles to efficiently unify product models into a family model and integrate the behavior of various products into a behavioral family model. Albeit reasonably effective, the exhaustive analysis of product lines is often infeasible due to the potentially exponential number of valid configurations. In this paper, we first present a family-based behavioral model learning techniques, called FFSMDiff. Subsequently, we report on our experience on learning family models by employing product sampling. Using 105 products of six product lines expressed in terms of Mealy machines, we evaluate the precision of family models learned from products selected from different settings of the T-wise product sampling criterion. We show that product sampling can lead to models as precise as those learned by exhaustive analysis and hence, reduce the costs for family model learning.
@article{damasceno_learning_2021, title = {Learning by sampling: learning behavioral family models from software product lines}, volume = {26}, issn = {1573-7616}, shorttitle = {Learning by sampling}, url = {https://doi.org/10.1007/s10664-020-09912-w}, doi = {10.1007/s10664-020-09912-w}, language = {en}, number = {1}, urldate = {2021-12-07}, journal = {Empirical Software Engineering}, author = {Damasceno, Carlos Diego Nascimento and Mousavi, Mohammad Reza and Simao, Adenilso da Silva}, month = jan, year = {2021}, pages = {4}, }
Best practices for artifact quality management in software engineering research

C. D. N. Damasceno

University of São Paulo (USP/ESALq), Dec 2021

MBA Thesis (in pt-BR)

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2020

Proceedings of the PhD Symposium at iFM’19on Formal Methods: Algorithms, Tools and Applications (PhD-iFM’19)

Ka I. Pun, Volker Stolz, Elahe Fazeldehkordi, and 10 more authors

Dec 2020

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Preface: This research report contains the proceedings of the PhD Symposium at iFM’19 on Formal Methods: Algorithms, Tools and Applications (PhD-iFM’19), which was held on 3 December, 2019 at Western Norway University of Applied Sciences, Bergen, Norway. The program of the symposium consisted of an invited talk by Andreas Griesmayer (ARM, Cambridge, UK) and 11 short presentations. Each short presentation received advices and feedbacks from a senior researcher. Among the 11 short presentations, 5 submitted their contributions in the form of extended abstracts, which were included in this report.

@inproceedings{Damasceno:2019:LearningFromFamilies:PhDiFM,
  type = {Report},
  title = {Proceedings of the {PhD} {Symposium} at {iFM}’19on {Formal} {Methods}: {Algorithms}, {Tools} and {Applications} ({PhD}-{iFM}’19)},
  copyright = {Navngivelse 4.0 Internasjonal},
  shorttitle = {Proceedings of the {PhD} {Symposium} at {iFM}’19on {Formal} {Methods}},
  url = {https://hvlopen.brage.unit.no/hvlopen-xmlui/handle/11250/2719437},
  language = {eng},
  urldate = {2021-12-07},
  institution = {Høgskulen på Vestlandet},
  author = {Pun, Ka I. and Stolz, Volker and Fazeldehkordi, Elahe and Owe, Olaf and Ramezanifarkhani, Toktam and Damasceno, Carlos Diego Nascimento and Ahishakiye, Faustin and Kristensen, Lars Michael and Tabar, Asmae Heydari and Bubel, Richard and Hähnle, Reiner and Sagemüller, Justus and Verdier, Olivier},
  year = {2020},
}

Learning finite state machine models of evolving systems: From evolution over time to variability in space

C. D. N. Damasceno

University of São Paulo (ICMC-USP), Jul 2020

PhD Thesis

DOI PDF Slides

2019

Learning to Reuse: Adaptive Model Learning for Evolving Systems

Carlos Diego Nascimento Damasceno, Mohammad Reza Mousavi, and Adenilso Silva Simao

In Integrated Formal Methods, Jul 2019

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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 partial-Dynamic L∗Mpartial-Dynamic LM∗}mathtt {partial}text {-}Dynamic~L^*_M} (∂L∗M∂LM∗}mathtt {}partial L^*_M}) that improves upon the state of the art by exploring observation tables on-the-fly to discard redundant prefixes and deprecated suffixes. Using 18 versions of the OpenSSL toolkit, we compare our proposed algorithm along with three adaptive algorithms. For the existing algorithms in the literature, our experiments indicate a strong positive correlation between number of membership queries and temporal distance between versions and; for our algorithm, we found a weak positive correlation between membership queries and temporal distance, as well, a significantly lower number of membership queries. These findings indicate that, compared to the state-of-the-art algorithms, our ∂L∗M∂LM∗}mathtt {}partial L^*_M} algorithm is less sensitive to software evolution and more efficient than the current approaches for adaptive learning.
@inproceedings{Damasceno:2019:LearningToReuse:iFM, address = {Cham}, series = {Lecture {Notes} in {Computer} {Science}}, title = {Learning to {Reuse}: {Adaptive} {Model} {Learning} for {Evolving} {Systems}}, isbn = {978-3-030-34968-4}, shorttitle = {Learning to {Reuse}}, url = {http://doi.org/10.1007/978-3-030-34968-4_8}, doi = {10.1007/978-3-030-34968-4_8}, language = {en}, booktitle = {Integrated {Formal} {Methods}}, publisher = {Springer International Publishing}, author = {Damasceno, Carlos Diego Nascimento and Mousavi, Mohammad Reza and da Silva Simao, Adenilso}, editor = {Ahrendt, Wolfgang and Tapia Tarifa, Silvia Lizeth}, year = {2019}, keywords = {Active learning, Mealy machines, Reactive systems, Software evolution, Software reuse}, pages = {138--156}, }
Trusted Autonomous Vehicles: an Interactive Exhibit

Hugo L. S. Araujo, Carlos Diego Nascimento Damasceno, Rayna Dimitrova, and 8 more authors

In 2019 IEEE International Conferences on Ubiquitous Computing Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), Oct 2019

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Recent surveys about autonomous vehicles show that the public is concerned about the safety consequences of system or equipment failures and the vehicles’ reactions to unexpected situations. We believe that informing about the technology and quality, e.g., safety and reliability, of autonomous vehicles is paramount to improving public expectations, perception and acceptance. In this paper, we report on the design of an interactive exhibit to illustrate (1) basic technologies employed in autonomous vehicles, i.e., sensors and object classification; and (2) basic principles for ensuring their quality, i.e., employing software testing and simulations. We subsequently report on a public engagement event involving this exhibit at the Royal Society Summer Science Exhibition 2019 in the exhibit titled "Trusted Autonomous Vehicles". We describe the process of designing and developing the artefacts used in our exhibit, the theoretical background associated to them, the design of our stand, and the lessons learned. The activities and findings of this study can be used by other educators and researchers interested in promoting trust in autonomous vehicles among the general public.
@inproceedings{AraujoEtal:2019:royalSociety, title = {Trusted {Autonomous} {Vehicles}: an {Interactive} {Exhibit}}, copyright = {All rights reserved}, shorttitle = {Trusted {Autonomous} {Vehicles}}, doi = {10.1109/IUCC/DSCI/SmartCNS.2019.00091}, booktitle = {2019 {IEEE} {International} {Conferences} on {Ubiquitous} {Computing} {Communications} ({IUCC}) and {Data} {Science} and {Computational} {Intelligence} ({DSCI}) and {Smart} {Computing}, {Networking} and {Services} ({SmartCNS})}, author = {Araujo, Hugo L. S. and Damasceno, Carlos Diego Nascimento and Dimitrova, Rayna and Kefalidou, Genovefa and Mehtarizadeh, Mehdi and Mousavi, Mohammad Reza and Onime, Jemima and Ringert, Jan Oliver and Rojas, Jose Miguel and Verdezoto, Nervo Xavier and Wali, Syed}, month = oct, year = {2019}, keywords = {program testing, Software quality, software testing, Autonomous vehicles, control engineering computing, equipment failures, human computer interaction, interactive exhibit, interactive systems, mobile robots, object classification, public engagement event, remotely operated vehicles, Royal Society Summer Science Exhibition 2019, safety consequences, Science communication, Science fair, sensors, software simulations, trust, trusted autonomous vehicles}, pages = {386--393}, url = {https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00091}, }
Learning from Difference: An Automated Approach for Learning Family Models from Software Product Lines

Carlos Diego Nascimento Damasceno, Mohammad Reza Mousavi, and Adenilso Simao

In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A, Oct 2019

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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-based testing and model checking for SPLs. Albeit reasonably efficient, the creation and maintenance of family models tend to be time consuming and error-prone, especially if there are crosscutting features. To tackle this issue, we introduce FFSMDiff, a fully automated technique to learn featured finite state machines (FFSM), a family-based formalism that unifies Mealy Machines from SPLs into a single representation. Our technique incorporates variability to compare and merge Mealy machines and annotate states and transitions with feature constraints. We evaluate our technique using 34 products derived from three different SPLs. Our results support the hypothesis that families of Mealy machines can be effectively merged into succinct FFSMs with fewer states, especially if there is high feature sharing among products. These indicate that FFSMDiff is an efficient family-based model learning technique.
@inproceedings{Damasceno:2019:LDA:3336294.3336307, author = {Damasceno, Carlos Diego Nascimento and Mousavi, Mohammad Reza and Simao, Adenilso}, title = {Learning from Difference: An Automated Approach for Learning Family Models from Software Product Lines}, year = {2019}, isbn = {9781450371384}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3336294.3336307}, doi = {10.1145/3336294.3336307}, booktitle = {Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A}, pages = {52–63}, numpages = {12}, keywords = {150% model, family model, software product lines, model learning}, location = {Paris, France}, series = {SPLC '19}, }

2018

Similarity testing for role-based access control systems

Carlos Diego Nascimento Damasceno, Paulo C. Masiero, and Adenilso Simao

Journal of Software Engineering Research and Development, Jan 2018

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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 techniques can be applied to improve fault detection along test execution. Recent studies have shown that similarity functions can be very efficient at prioritizing test cases. This technique is named similarity testing and assumes the hypothesis that resembling test cases tend to have similar fault detection capabilities. Thus, there is no gain from similar test cases, and fault detection ratio can be improved if test diversity increases.
@article{N.Damasceno2018, author = {Damasceno, Carlos Diego Nascimento and Masiero, Paulo C. and Simao, Adenilso}, title = {Similarity testing for role-based access control systems}, journal = {Journal of Software Engineering Research and Development}, year = {2018}, month = jan, day = {17}, volume = {6}, number = {1}, pages = {1}, issn = {2195-1721}, doi = {10.1186/s40411-017-0045-x}, url = {https://doi.org/10.1186/s40411-017-0045-x}, }

Data Analysis of Multiplex Sequencing at SOLiD Platform: A Probabilistic Approach to Characterization and Reliability Increase

Fábio Manoel França Lobato, Carlos Diego N. Damasceno, Daniela Soares Leite, and 5 more authors

American Journal of Molecular Biology (AJMB), Jan 2018

DOI Bib PDF

@article{FranaLobato2018,
  doi = {10.4236/ajmb.2018.81003},
  url = {https://doi.org/10.4236/ajmb.2018.81003},
  year = {2018},
  publisher = {Scientific Research Publishing},
  volume = {08},
  number = {01},
  pages = {26--38},
  author = {Lobato, Fábio Manoel França and Damasceno, Carlos Diego N. and Leite, Daniela Soares and Ribeiro-dos-Santos, Ândrea Kelly and Darnet, Sylvain and Francês, Carlos Renato and Vijaykumar, Nandamudi Lankalapalli and de Santana, Ádamo Lima},
  title = {Data Analysis of Multiplex Sequencing at SOLiD Platform: A Probabilistic Approach to Characterization and Reliability Increase},
  journal = {American Journal of Molecular Biology (AJMB)},
}

2017

Testing enviroments and optimization: Amdocs

Adenilso Silva Simao, Andre Carvalho, Carlos Diego Nascimento Damasceno, and 26 more authors

In 3rd Workshop CeMEAI of Mathematical Solutions for Industrial Problems, Jan 2017

Bib PDF Website

@inproceedings{simaoetal:2017:wsmpi,
  author = {da Silva Simao, Adenilso and Carvalho, Andre and Damasceno, Carlos Diego Nascimento and dos Santos, Danniany and dos Santos Moreira, Edson and Tomita, Fabio and Maia, Giovana Sachett and Hortencio, Hanna Pamplona and Nakel, Idan and Peronti, Isabela and Pereira, Jamielli Tomaz and Siqueira, João Paulo Guardabaxo and Cutigi, Jorge Francisco and Saviniec, Landir and Mundim, Leandro Resende and e Moreira, Lucas Esperancini Moreira and de Freitas, Luis Eduardo and Cherri, Luiz Henrique and dos Santos, Maria Carolina and de Oliveira, Marina Barisa and Junior, Misael Costa and Chandekar, Pratibha and Gonçalves, Raínne Florisbelo and Butkeraites, Renan Brito and Gesuatto, Ricardo and Goel, Rohit and Mendonça, Sergio and de Andrade, Stevão Alves and Cardoso, Thais},
  booktitle = {3rd Workshop CeMEAI of Mathematical Solutions for Industrial Problems},
  title = {Testing enviroments and optimization: Amdocs},
  year = {2017},
  publisher = {CEPID/CeMEAI},
  location = {São Carlos-SP, Brazil},
}

Inference of Family Models for Software Product Line Testing

Carlos Diego Nascimento Damasceno, and Adenilso Silva Simao

In 1o. Encontro Paulista dos Pós-graduandos em Computação (EPPC), Jan 2017

Poster
Um Algoritmo Paralelo para Priorização de Testes Baseada em Similaridade usando OpenMPI

Carlos Diego Nascimento Damasceno, Paulo S. L. Souza, and Adenilso Simao

In Anais Estendidos da Escola Regional de Alto Desempenho de São Paulo (ERAD-SP), Jan 2017

PDF Poster Slides Website

2016

Evaluating Test Characteristics and Effectiveness of FSM-Based Testing Methods on RBAC Systems

Carlos Diego Nascimento Damasceno, Paulo Cesar Masiero, and Adenilso Simao

In Proceedings of the 30th Brazilian Symposium on Software Engineering, Jan 2016

(3rd Best Paper Award)

Abs DOI Bib PDF Slides Website

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 can reduce the overall test suite length for random FSMs. However, since the similarity between random FSMs and these specifying RBAC mechanisms is unclear, these outcomes cannot be necessarily generalized to RBAC. In this paper, we compare the characteristics and effectiveness of test suites generated by traditional and recent FSM testing methods for RBAC policies specified as FSM models. The methods W, HSI and SPY were applied on RBAC policies specified as FSMs and the test suites obtained were evaluated considering test characteristics (number of resets, average test case length, and test suite length) and effectiveness on the RBAC fault domain. Our results corroborate some outcomes of previous investigations in which test suites presented different characteristics. On average, the SPY method generated test suites with 32% less resets, average test case length 78% greater than W and HSI, and overall length 46% lower. There were no differences among FSM testing methods for RBAC regarding effectiveness. However, the SPY method significantly reduced the overall test suite length and the number of resets.
@inproceedings{10.1145/2973839.2973849, author = {Damasceno, Carlos Diego Nascimento and Masiero, Paulo Cesar and Simao, Adenilso}, title = {Evaluating Test Characteristics and Effectiveness of FSM-Based Testing Methods on RBAC Systems}, year = {2016}, isbn = {9781450342018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2973839.2973849}, doi = {10.1145/2973839.2973849}, booktitle = {Proceedings of the 30th Brazilian Symposium on Software Engineering}, pages = {83–92}, numpages = {10}, keywords = {Finite state machine, Experiments, Role-Based Access Control (RBAC), Conformance Testing}, location = {Maring\'{a}, Brazil}, note = {(3rd Best Paper Award)}, series = {SBES '16}, }
Evaluating finite state machine based testing methods on RBAC systems

C. D. N. Damasceno

University of São Paulo (ICMC-USP), May 2016

MSc Thesis

DOI PDF

2015

A Systematic Literature Review on Knowledge Representation Approaches for Systems-of-Systems

Gabriel Abdalla, Carlos Diego Nascimento Damasceno, Milena Guessi, and 2 more authors

In 2015 IX Brazilian Symposium on Components, Architectures and Reuse Software, Sep 2015

(3rd Best Paper Award)

Abs DOI PDF Slides

Systems-of-Systems are a class of systems composed of diverse, independent constituent systems. Together, these constituents can accomplish missions that otherwise could not be performed by any of them separately. In another perspective, knowledge representation approaches can assist in the establishment of a common understanding in this field by formalizing and standardizing the main terms and concepts adopted. In spite of the relevance of SoS, a consolidated terminology which could support the community working with such systems is still missing. Furthermore, the multiplicity of stakeholders, technologies, and expertise involved in an SoS makes the need of a common understanding even more imperative. In this study, we report on the main findings of a systematic literature review covering knowledge representation approaches in the SoS field. With this study, we are able to present a comprehensive panorama of the knowledge representation approaches that are currently adopted. Even though a consolidated terminology is not available yet, such panorama can be helpful for devising a common, comprehensive terminology for the SoS field. Therefore, we conclude this paper with directions for future work.

2014

Uma Revisão Sistemática em Teste de Segurança Baseado em Modelos

Carlos Diego Nascimento Damasceno, Márcio Eduardo Delamaro, and Adenilso da Silva Simão

In Anais do Workshop Brasileiro de Testes de Software Automatizados e Sistemático - CBSoft - Congresso Brasileiro de Software: Teoria e Prática, Sep 2014

PDF Slides

SimCleaner - Sistema de Padronizacao de Bases de Dados utilizando Funcoes de Similaridade

C. D. N. Damasceno, E. R. Moutinho, F. M. F. Lobato, and 3 more authors

Sep 2011

PDF
Abordagem probabilística para análise de confiabilidade de dados gerados em sequenciamentos multiplex na plataforma ABI SOLiD

F. M. F. Lobato, C. D. Damasceno, P. L. Machado, and 5 more authors

In Anais do XLIII Simpósio Brasileiro de Pesquisa Operacional (SBPO 2011), Sep 2011

PDF Website