No abstract

Behaviour models facilitate the understanding and analysis of software systems by providing an abstract view of their behaviours and also by enabling the use of validation and verification techniques to detect errors. However, depending on the size and complexity of these systems, constructing models may not be a trivial task, even for experienced developers. Model extraction techniques can automatically obtain models from existing code, thus reducing the effort and expertise required of engineers and helping avoid errors often present in manually constructed models. Existing approaches for model extraction often fail to produce faithful models, either because they only consider static information, which may include infeasible behaviours, or because they are based only on dynamic information, thus relying on observed executions, which usually results in incomplete models. This paper describes a model extraction approach based on the concept of contexts, which are abstractions of concrete states of a program, combining static and dynamic information. Contexts merge some of the advantages of using either type of information and, by their combination, can overcome some of their problems. The approach is partially implemented by a tool called LTS Extractor (LTSE), which translates information collected from execution traces produced by instrumented Java code to Labelled Transition Systems (LTS), which can be analysed in an existing verification tool. Results from case studies are presented and discussed, showing that, considering a level of abstraction and a set of execution traces, the produced models are correct descriptions of the programs from which they were extracted. Thus, they can be used for a variety of analyses, such as program understanding, validation, verification, and evolution.

Abstract. Behaviour models facilitate the analysis of software systems using model-checking tools to detect errors and generate counterexamples. Such models can be generated from existing implementations using a model extraction process. This process should guarantee that an extracted model is a faithful representation of the system, so that analysis results may be trusted. This paper discusses the formal foundations of our model extraction process based on contexts. Contexts are abstractions of concrete states of a system, providing valuable information about dependencies between actions. Models are generated by a tool called LTS Extractor and can be refined to improve correctness by augmenting context information. This refinement process eliminates some false negatives and is property-preserving. Completeness of the models depends on the coverage provided by a set of traces describing behaviours of the system. We discuss the faithfulness of our models and results of two case studies.

No abstract

No abstract