CDN (Diego) Damasceno

selected publications

1. Quality {Guidelines} for {Research} {Artifacts} in {Model}-{Driven} {Engineering}

   Damasceno, Carlos Diego Nascimento, and Strüber, Daniel

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

   DOI Abs arXiv Bib PDF Supp Slides Website

   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,
     selected = true,
     bibtex_show = true,
     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},
     pdf = {damascenostruber2021_models_article.pdf},
     slides = {damascenostruber2021_models_slide.pdf},
     website = {https://mdeartifacts.github.io/},
     arxiv = {2108.04652},
     supp = {damascenostruber2021_models_artifact.pdf}
   }

2. Learning by sampling: learning behavioral family models from software product lines

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

   Empirical Software Engineering 2021

   DOI Abs Bib PDF Slides Website

   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},
     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},
     pdf = {damascenoetal2020_emse.pdf},
     slides = {EMSE2021_slides.pptx},
     website = {https://github.com/damascenodiego/learningFFSM/tree/master/experiments/emse2020},
     bibtex_show = true
   }

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

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

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

   DOI Abs 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,
     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
   }