Abstracting Models from Execution Traces for Performing Formal Verification

Bodhuin, Thierry; Pagnozzi, Federico; Santone, Antonella; Tortorella, Maria; Villani, Maria Luisa

doi:10.1007/978-3-642-10619-4_18

Cited by 2 publications

(2 citation statements)

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“…The work of Bodhuin et al [7], like ours, attempts to extract behaviour models based on traces collected from instrumented Java programs. However, they obtain the models from the Java bytecodes.…”

Section: Related Workmentioning

confidence: 99%

“…There has been many approaches that try to overcome the model construction problem, such as [13] [2] [45] [41] [7], [33], [10], and [29], [57], that we could use to create the model for our code. These techniques can be divided into two groups: static approaches, which build models based on information from the source or compiled code, and dynamic approaches, which infer models from samples of execution.…”

Section: Introductionmentioning

confidence: 99%

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Using contexts to extract models from code

2015

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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.

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Section: Related Workmentioning

confidence: 99%