@inproceedings{DBLP:conf/re/DellAnnaDD18,
  author    = {Davide Dell'Anna and
               Fabiano Dalpiaz and
               Mehdi Dastani},
  title     = {Validating Goal Models via Bayesian Networks},
  booktitle = {Proceedings of the 5th International Workshop on Artificial Intelligence for Requirements
               Engineering, AIRE@RE 2018},
  pages     = {39--46},
  year      = {2018},
  url_Link       = {https://doi.org/10.1109/AIRE.2018.00012},
  url_Paper      = {2018_AIRE/AIRE18_DellAnna.pdf},
  url_Slides= {2018_AIRE/AIRE18_DellAnna_Slides.pdf},
  doi       = {10.1109/AIRE.2018.00012},
  keywords  = {Goal Models, Bayesian Networks, Assumptions, Validation, Data Driven Supervision Of Autonomous Systems},
  timestamp = {Mon, 15 Jun 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/re/DellAnnaDD18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  abstract = {Goal models are an example of requirement modeling language that has been applied to support the runtime monitoring and diagnosis of software systems and to steer self-adaptive systems. When creating a goal model, requirement engineers make <i>assumptions</i> concerning how the goals relate to each other and when they should be considered as satisfied. In dynamic environments, however, the assumptions made in the model may be (or become) invalid. This may result in a system that does not satisfy the stakeholders' needs and, when the model is used in adaptive systems, ineffective reconfigurations. Only few and preliminary works address the automated validation of goal or requirement models. In this paper we propose the use of probabilistic models (Bayesian Networks) to determine the validity of the assumptions underlying a goal model. We employ empirical data and probabilistic inference to automatically determine a quantitative degree of validity of goal model assumptions. We illustrate the approach on a smart traffic scenario.}
}

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We illustrate the approach on a smart traffic scenario.","bibtex":"@inproceedings{DBLP:conf/re/DellAnnaDD18,\n author = {Davide Dell'Anna and\n Fabiano Dalpiaz and\n Mehdi Dastani},\n title = {Validating Goal Models via Bayesian Networks},\n booktitle = {Proceedings of the 5th International Workshop on Artificial Intelligence for Requirements\n Engineering, AIRE@RE 2018},\n pages = {39--46},\n year = {2018},\n url_Link = {https://doi.org/10.1109/AIRE.2018.00012},\n url_Paper = {2018_AIRE/AIRE18_DellAnna.pdf},\n url_Slides= {2018_AIRE/AIRE18_DellAnna_Slides.pdf},\n doi = {10.1109/AIRE.2018.00012},\n keywords = {Goal Models, Bayesian Networks, Assumptions, Validation, Data Driven Supervision Of Autonomous Systems},\n timestamp = {Mon, 15 Jun 2020 01:00:00 +0200},\n biburl = {https://dblp.org/rec/conf/re/DellAnnaDD18.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org},\n abstract = {Goal models are an example of requirement modeling language that has been applied to support the runtime monitoring and diagnosis of software systems and to steer self-adaptive systems. 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