Tavassoli, Shaghayegh; Damasceno, Carlos Diego N.; Khosravi, Ramtin; Mousavi, Mohammad Reza
Adaptive Behavioral Model Learning for Software Product Lines Inproceedings
In: Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A, pp. 142–153, Association for Computing Machinery, Graz, Austria, 2022, ISBN: 9781450394437.
@inproceedings{10.1145/3546932.3546991,
title = {Adaptive Behavioral Model Learning for Software Product Lines},
author = {Shaghayegh Tavassoli and Carlos Diego N. Damasceno and Ramtin Khosravi and Mohammad Reza Mousavi},
url = {https://doi.org/10.1145/3546932.3546991},
doi = {10.1145/3546932.3546991},
isbn = {9781450394437},
year = {2022},
date = {2022-03-01},
booktitle = {Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A},
pages = {142–153},
publisher = {Association for Computing Machinery},
address = {Graz, Austria},
series = {SPLC '22},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Moezkarimi, Zahra; Ghassemi, Fatemeh; Mousavi, Mohammad Reza
A policy-aware epistemic framework for social networks Journal Article
In: Journal of Logic and Computation, 32 (6), pp. 1234-1271, 2022, ISSN: 0955-792X.
@article{10.1093/logcom/exac025,
title = {A policy-aware epistemic framework for social networks},
author = {Zahra Moezkarimi and Fatemeh Ghassemi and Mohammad Reza Mousavi},
url = {https://doi.org/10.1093/logcom/exac025},
doi = {10.1093/logcom/exac025},
issn = {0955-792X},
year = {2022},
date = {2022-03-01},
journal = {Journal of Logic and Computation},
volume = {32},
number = {6},
pages = {1234-1271},
abstract = {We provide a semantic framework to specify information propagation in social networks; our semantic framework features both the operational description of information propagation and the epistemic aspects in social networks. In our framework, based on annotated labelled transition systems, actions are decorated with function views to specify different types of announcements. Our function views enforce various common types of local privacy policies, i.e. those policies concerning a single action. Furthermore, we specify global privacy policies, those concerning multiple actions, using a combination of modal $mu $-calculus and epistemic logic. To illustrate the applicability of our framework, we apply it to the specification of a real-world case study. As a fundamental property for the epistemic aspect of our semantic model, we prove that its indistinguishability relations are equivalence relations, namely they are reflexive, symmetric and transitive. We also study the complexity bounds for the model-checking problem concerning a subset of our logic and show that model checking is PSPACE-complete for the studied subset.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Townsend, Beverley; Paterson, Colin; Arvind, TT; Nemirovsky, Gabriel; Calinescu, Radu; Cavalcanti, Ana; Habli, Ibrahim; Thomas, Alan
From Pluralistic Normative Principles to Autonomous-Agent Rules Journal Article
In: Minds and Machines, pp. 1–33, 2022.
@article{townsend2022pluralistic,
title = {From Pluralistic Normative Principles to Autonomous-Agent Rules},
author = {Beverley Townsend and Colin Paterson and TT Arvind and Gabriel Nemirovsky and Radu Calinescu and Ana Cavalcanti and Ibrahim Habli and Alan Thomas},
year = {2022},
date = {2022-03-01},
journal = {Minds and Machines},
pages = {1--33},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}