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,selected=true,title={Learning by sampling: learning behavioral family models from software product lines},volume={26},issn={1573-7616},url={https://doi.org/10.1007/s10664-020-09912-w},doi={10.1007/s10664-020-09912-w},language={en},number={1},urldate={2021-01-15},journal={Empirical Software Engineering},author={Damasceno, Carlos Diego Nascimento and Mousavi, Mohammad Reza and Simao, Adenilso da Silva},month=jan,year={2021},pages={4},pdf={damascenoetal2020_emse.pdf},slides={EMSE2021_slides.pptx},website={https://github.com/damascenodiego/learningFFSM/tree/master/experiments/emse2020},bibtex_show=true}
2020
Learning From Families: Inferring Behavioral Variability From Software Product Lines
Damasceno, Carlos Diego N.
In Proceedings of the PhD Symposium at iFM’19 on Formal Methods: Algorithms, Tools and Applications (PhD-iFM’19)
2020
Family models are behavioral specifications extended with
variability constraints that enable
efficient model-based analysis of software product lines (SPL).
Albeit reasonably efficient,
the creation and maintenance of family models are time-consuming and error-prone,
especially if there are large models or crosscutting features.
In this PhD project,
we investigate the problem of learning family models from SPLs.
Our initial contributions are two-fold:
(1) partial-Dynamic L*M, a novel adaptive algorithm to speed up automata learning by exploring models from alternative software versions on-the-fly; and
(2) FFSM_Diff, a fully automated technique to learn family models by comparing, merging and annotating finite state machines with variability constraints.
Our experiments have shown that our techniques are more efficient than the state-of-the-art of adaptive learning in terms of queries and that
succinct family models with fewer states can be learnt, especially if there is high feature reuse.
We envisage that our studies can leverage model-based techniques to cases where models are non-existent or outdated and
will scale better than independently exploring several versions of evolving systems or product models from configurable systems.
@inproceedings{Damasceno:2019:LearningFromFamilies:PhDiFM,author={Damasceno, Carlos Diego N.},title={Learning From Families: Inferring Behavioral Variability From Software Product Lines},booktitle={Proceedings of the PhD Symposium at iFM’19 on Formal Methods: Algorithms, Tools and Applications (PhD-iFM’19)},year={2020},location={Bergen, Norway},url={https://hvlopen.brage.unit.no/hvlopen-xmlui/handle/11250/2719437},website={https://hvlopen.brage.unit.no/hvlopen-xmlui/handle/11250/2719437},language={eng},date={2020-12-28},institution={Høgskulen på Vestlandet},pdf={damasceno_phdifm2019.pdf},slides={damasceno_phdifm2019_slide.pdf},bibtex_show=true}
Learning finite state machine models of evolving systems: From evolution over time to variability in space
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 {\$}{\$}{\backslash}mathtt {\{}partial{\backslash}text {\{}-{\}}Dynamic{\textasciitilde}L^*{\_}M{\}}{\$}{\$} ({\$}{\$}{\backslash}mathtt {\{}{\backslash}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 {\$}{\$}{\backslash}mathtt {\{}{\backslash}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,author={Damasceno, Carlos Diego N. and Mousavi, Mohammad Reza and Simao, Adenilso da Silva},editor={Ahrendt, Wolfgang and Tapia Tarifa, Silvia Lizeth},title={Learning to Reuse: Adaptive Model Learning for Evolving Systems},booktitle={Integrated Formal Methods},year={2019},publisher={Springer International Publishing},address={Cham},pages={138--156},isbn={978-3-030-34968-4},url={http://doi.org/10.1007/978-3-030-34968-4_8},doi={10.1007/978-3-030-34968-4_8},pdf={damascenoetal_ifm2019.pdf},slides={damascenoetal_ifm2019_slide.pdf},website={https://damascenodiego.github.io/DynamicLstarM/},bibtex_show=true}
Trusted Autonomous Vehicles: an Interactive Exhibit
Araujo, H. L. S.,
Damasceno, C. D. N.,
Dimitrova, R.,
Kefalidou, G.,
Mehtarizadeh, M.,
Mousavi, M. R.,
Onime, J.,
Ringert, J. O.,
Rojas, J. M.,
Verdezoto, N. X.,
and Wali, S.
In 2019 IEEE International Conferences on Ubiquitous Computing Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)
2019
@inproceedings{AraujoEtal:2019:royalSociety,author={Araujo, H. L. S. and Damasceno, C. D. N. and Dimitrova, R. and Kefalidou, G. and Mehtarizadeh, M. and Mousavi, M. R. and Onime, J. and Ringert, J. O. and Rojas, J. M. and Verdezoto, N. X. and Wali, S.},booktitle={2019 IEEE International Conferences on Ubiquitous Computing Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)},eventtitle={2019 {IEEE} International Conferences on Ubiquitous Computing Communications ({IUCC}) and Data Science and Computational Intelligence ({DSCI}) and Smart Computing, Networking and Services ({SmartCNS})},title={Trusted Autonomous Vehicles: an Interactive Exhibit},year={2019},pages={386-393},publisher={IEEE},location={Shenyang, China},doi={10.1109/IUCC/DSCI/SmartCNS.2019.00091},url={https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00091},website={https://driverleics.github.io/},pdf={araujoetal_dsci2019.pdf},bibtex_show=true}
Learning from Difference: An Automated Approach for Learning Family Models from Software Product Lines
Damasceno, Carlos Diego N.,
Mousavi, Mohammad Reza,
and Simao, Adenilso
In Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A
2019
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 N. 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},pdf={damascenoetal_splc2019.pdf},slides={damascenoetal_splc2019_slide.pdf},website={https://github.com/damascenodiego/learningFFSM/tree/master/experiments/splc2019},supp={damascenoetal_splc2019_artifact.pdf},bibtex_show=true}
2018
Similarity testing for role-based access control systems
Damasceno, Carlos Diego N.,
Masiero, Paulo C.,
and Simao, Adenilso
Journal of Software Engineering Research and Development
2018
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 N. 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},pdf={damascenoetal2017_jserd.pdf},bibtex_show=true}
Data Analysis of Multiplex Sequencing at SOLiD Platform: A Probabilistic Approach to Characterization and Reliability Increase
Lobato, Fábio Manoel França,
Damasceno, Carlos Diego N.,
Leite, Daniela Soares,
Ribeiro-dos-Santos, Ândrea Kelly,
Darnet, Sylvain,
Francês, Carlos Renato,
Vijaykumar, Nandamudi Lankalapalli,
and Santana, Ádamo Lima
@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)},note={[[PDF]](/publications/pdf/lobatoetal2018_solidprobabilistic.pdf)},pdf={lobatoetal2018_solidprobabilistic.pdf},bibtex_show=true}
2017
Testing enviroments and optimization: Amdocs
Silva Simao, Adenilso,
Carvalho, Andre,
Damasceno, Carlos Diego Nascimento,
Santos, Danniany,
Santos Moreira, Edson,
Tomita, Fabio,
Maia, Giovana Sachett,
Hortencio, Hanna Pamplona,
Nakel, Idan,
Peronti, Isabela,
Pereira, Jamielli Tomaz,
Siqueira, João Paulo Guardabaxo,
Cutigi, Jorge Francisco,
Saviniec, Landir,
Mundim, Leandro Resende,
Moreira, Lucas Esperancini Moreira,
Freitas, Luis Eduardo,
Cherri, Luiz Henrique,
Santos, Maria Carolina,
Oliveira, Marina Barisa,
Junior, Misael Costa,
Chandekar, Pratibha,
Gonçalves, Raínne Florisbelo,
Butkeraites, Renan Brito,
Gesuatto, Ricardo,
Goel, Rohit,
Mendonça, Sergio,
Andrade, Stevão Alves,
and Cardoso, Thais
In 3rd Workshop CeMEAI of Mathematical Solutions for Industrial Problems
2017
@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},website={https://drive.google.com/file/d/1DxkyPkkUqPZaHIHDtgYYrZ_wAEC6fbk2/view},pdf={simaoetal2017_wsmpi.pdf},bibtex_show=true}
Inference of Family Models for Software Product Line Testing
Damasceno, Carlos Diego Nascimento,
and Silva Simao, Adenilso
In 1o. Encontro Paulista dos Pós-graduandos em Computação (EPPC)
2017
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,selected=true,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},series={SBES '16},pdf={damascenoetal_sbes2016.pdf},slides={damascenoetal_sbes2016_slide.pdf},website={https://github.com/damascenodiego/rbac-bt},bibtex_show=true}
Evaluating finite state machine based testing methods on RBAC systems
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
Damasceno, Carlos Diego Nascimento,
Delamaro, Márcio Eduardo,
and Simão, Adenilso da Silva
In Anais do Workshop Brasileiro de Testes de Software Automatizados e Sistemático - CBSoft - Congresso Brasileiro de Software: Teoria e Prática
2014