@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}, abstract = {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.}, 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}, note = "[[PDF]](/publications/pdf/damascenoetal2020_emse.pdf) [[Slide]](/publications/pdf/EMSE2021_slides.pptx)", } @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", abstract="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.", issn="2195-1721", doi="10.1186/s40411-017-0045-x", url="https://doi.org/10.1186/s40411-017-0045-x", note = "[[PDF]](/publications/pdf/damascenoetal2017_jserd.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 = {Fábio Manoel França Lobato and Carlos Diego N. Damasceno and Daniela Soares Leite and Ândrea Kelly Ribeiro-dos-Santos and Sylvain Darnet and Carlos Renato Francês and Nandamudi Lankalapalli Vijaykumar and Ádamo Lima de Santana}, 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)", }