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Analyse et optimisation d'algorithmes pour l'inférence de modèles de composants logiciels

Muhammad Naeem Irfan 1
1 VASCO - Validation de Systèmes, Composants et Objets logiciels
LIG - Laboratoire d'Informatique de Grenoble
Abstract : Components-Off-The-Shelf (COTS) are used for rapid and cost effective developmentof software systems. It is important to test the correct functioning of COTS in new environment. For third party software components source code, completes pecifications and models are not available. In literature such systems are referred as black box software components. Their proper functioning in new environment can be tested with black box testing techniques like, comparison testing, fuzz testing, Model based testing. For Model based software testing, software models are required, which represent the desired behavior of a system under test (SUT). A software model shows that a certain set of inputs are applicable to the SUT and how it behaves when these inputs are applied under different circumstances. For software black box systems, models can be learned from behavioral traces, available specifications, knowledge of experts and other such sources. The software models steer the testing of software systems. The model inference algorithms extractstructural and design information of a software system and present it as a formal model. The learned abstract software model is consistent with the behavior of the particular software system. However, the learned models are rarely complete and it is difficult to calculate the number of tests required to learn precise and complete model of a software system. The thesis provides analysis and improvements on the Mealy adaptation of the model inference algorithm L* [Angluin 87]. It targets at reducing the number oftests required to learn models of software systems. The Mealy adaptation of thealgorithm L* requires learning models by asking two types of tests. First type oftests are asked to construct models i.e. output queries, whereas the second type is used to test the correctness of these models i.e. counterexamples. The algorithm uses an observation table to record the answers of output queries. Processing a counterexample may require a lot of output queries. The thesis addresses this problem and proposes a technique which processes the counterexamples efficiently. We observe that while learning the models of software systems asking output queries for all of the observation table rows and columns is not required. We propose a learning algorithm that avoids asking output queries for such observationtable rows and columns. In some cases to learn a software model, searching for counterexamples may govery expensive. We have presented a technique which learns the software models without asking and processing counterexamples. But this may add many columns to the observation table and in reality we may not require to ask output queries for all of the table cells. This technique asks output queries by targeting to avoid asking output queries for such cells. These contributions reduce the number of tests required to learn software models, thus improving the worst case learning complexity. We present the tool RALT which implements our techniques and the techniques are validated by inferring the examples like buffers, vending machines, mutual exclusion protocols and schedulers.
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Muhammad Naeem Irfan. Analyse et optimisation d'algorithmes pour l'inférence de modèles de composants logiciels. Apprentissage [cs.LG]. Université de Grenoble, 2012. Français. ⟨NNT : 2012GRENM072⟩. ⟨tel-00767894⟩

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