Constraint programming allows to model many kind of problems with efficient solving methods. However, its complexity has increased these last years and its use, notably to model problems, has become limited to people with a fair expertise in the domain. This thesis deals with automating the modeling task in constraint programming. Methods already exist, with encouraging results, but many requirements are debatable. In a first part, we propose to avoid the limitation consisting, for the user, in providing solutions of the problem she aims to solve. As a replacement of these solutions, the user has to provide solutions of closed problem, i.e problem with same semantic but where variables and domains can be different. To handle this kind of data, we acquire, thanks to inductive logic programming, a more abstract model than the constraint network. When this model is learned, it is translated in the very constraint network the user aims to model. We show the limitations of learning method to build such a model due to pathological problems and explain the new algorithm we have developed to build these abstract models. In a second part, we are interesting in the possibility to the user to not provide any examples. Starting with a CSP without constraints, our method consists in solving the problem the user wants in a standard way. Thanks to a search tree, we affect to each variable a value. When our tool cannot decide if the current partial affectation is correct or not, we ask to the user, with yes/no queries, to guide the search. These queries allow to find constraints to add to the model and then to improve the quality of the search.