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Teaching formal methods is notoriously difficult and is linked to motivation problems among the students; we think that formal methods need to be supported by adequate tools to get better acceptance from the students. One of the goals of the thesis is to build a practical tool to help students to deeply understand the classical programming methodology based on specifications, loop invariants, and decomposition into subproblems advocated by Dijkstra, Gries, and Hoare to name only a few famous computer scientists. Our motivation to build this tool is twofold. On the one hand, we demonstrate that existing verification tools (e.g., ESC/Java, Spark, SMV) are too complex to be used in a pedagogical context; moreover they often lack completeness, (and sometimes, even soundness). On the other hand teaching formal (i.e., rigorous) program construction with pen and paper does not motivate students at all. Thus, since students love to use tools, providing them with a tool that checks not only their programs but also their specifications and the structure of their reasoning seemed appealing to us. Obviously, building such a system is far from an easy task. It may even be thought completely unfeasible to experts in the field. Our approach is to restrict our ambition to a very simple programming language with simple types (limited to finite domains) and arrays. In this context, it is possible to specify problems and subproblems, both clearly and formally, using a specific assertion language based on mathematical logic. It appears that constraint programming over finite domains is especially convenient to check the kind of verification conditions that are needed to express the correctness of imperative programs. However, to conveniently generate the constraint problems equivalent to a given verification condition, we wish to have at hand a powerful language that allows us to interleave constraints generation, constraints solving, and to specify a distribution strategy to overcome the incompleteness of the usual consistency techniques used by finite domain constraint programming. We show in this thesis that the Oz language includes all programming mechanisms needed to reach our goals. Such a tool has been fully implemented and is intended to provide interesting feedback to students learning the programming method: it detects programming and/or reasoning errors and it provides typical counter-examples. We argue that our system is adapted to our pedagogical context and we report on experiments of using the tool with students in a third year programming course.

Logic programming is appealing since it allows the programmer to concentrate on the meaning of the problem to be solved. Unfortunately, for efficiency reasons, the declarative and operational natures of Prolog do not coincide. Prolog uses an incomplete depth-first search rule, unifications and negations may be unsound, and there are extralogical features like the cut or dynamic predicates. Methodologies have been proposed to construct operationally correct and efficient Prolog code. Researchers have designed methods to automate the verification of operational properties on which optimisation of logic programs can be based. A few tools have been implemented but there is a lack of a unified framework. <P> The goal and topic of this thesis is the design, implementation and evaluation of an abstract interpretation framework of Prolog to integrate state-of-the-art techniques. The analyser is based on an original proposal that defines the notion of abstract sequence, which allows one to verify many desirable operational properties of a logic procedure. The properties include types, modes, sharing of terms, proving termination, linear relations between the size of input/output terms and the number of solutions to a call. A single global analysis is performed, and abstract sequences are derived at each program point. <P> In this thesis, we implement and evaluate the original framework, and, more importantly, we overcome its limitations to make it accurate and usable in practice: the improved framework accepts any Prolog code with modules, new abstract domains and operations are added, and the language of specifications is more expressive. We also design and implement an optimiser that generates specialised code. The optimiser uses the abstract information to safely apply source-to-source transformations. Code transformations include clause and literal reordering, introduction of cuts, and removal of redundant literals. The optimiser follows a precise strategy to choose the most rewarding transformations in best order.