Towards Faithful Model Extraction Based on Contexts

Duarte, Lucio Mauro; Kramer, Jeff; Uchitel, Sebastián

doi:10.1007/978-3-540-78743-3_9

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…6. It describes that a document was opened (lines 1-10), an invalid option was entered (lines [11][12][13][14][15], and then the next input was the command to edit the document (lines [16][17][18][19][20][21][22][23]. Lines 24-33 represent the execution of option print, followed by a document save (lines [34][35][36][37][38][39][40][41][42].…”

Section: Information Gatheringmentioning

confidence: 99%

“…Contexts have been successfully used to extract models from sequential and concurrent systems [17] and the analyses carried out using our models in a number of case studies [18] have demonstrated that they are correct abstractions of the systems they describe at a certain level of abstraction and according to a set of observed behaviours (traces). Hence, the extracted models are useful for many analysis purposes.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, new behaviours can be incrementally included in the model in order to improve completeness, without the need of rebuilding the whole model. The approach is partially supported by a tool called LTS Extractor (LTSE) [17] and the generated models can readily serve as inputs to the LTSA tool [43], which supports visualisation, model execution, and verification of temporal properties.…”

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: Information Gatheringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using contexts to extract models from code

2015

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“…The Labelled Transition System Extractor (LTSE) tool [6] extracts, from an annotated Java code, the context information as a table of contexts ( Figure 3) and a set of traces of contexts ( Figure 4), which are used in the method described in [6], to generate models of Labeled Transition Systems (LTS) [8]. Our approach uses the data extracted by the LTSE tool to generate a graph grammar with rules that represent the possible transitions of the program.…”

Section: A Model Extractionmentioning

confidence: 99%

Specification of Models Based on Contexts using Graph Grammars

Oliveira

Ribeiro

Duarte

et al. 2013

2013 2nd Workshop-School on Theoretical Computer Science

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This paper presents an approach for model specification in the context of Model Checking, using graph grammars. The choice for this formal method is justified by the visual language provided by Graph Grammar and the possibility of using it to check properties of a system. Our approach is based on the analysis of a graph grammar obtained from trace information generated by Java applications. The process of creating such graph grammar from execution traces follows the main ideas of an existing approach that allows the generation of labeled transition systems from Java code. The main objective of this work is to find a way to automate the process of generating a graph grammar, using the information extracted from the source code and execution traces. For this, we developed an algorithm that maps system information, such as values of variables and possible transitions between states of a program, to rules of a graph grammar. To evaluate the methodology used in our approach, we conducted two experiments with toy applications and discuss the results obtained.

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“…In this case models are heavily dependent on parameters that must be provided a-priori by domain experts or extracted by other similar systems. In [11] the formal foundations of model extraction process based on contexts is discussed. The correctness of the models is affected by the set of attributes used as the system state, while in our approach is guaranteed for safety properties.…”

Section: Conclusion and Related Workmentioning

confidence: 99%

Abstracting Models from Execution Traces for Performing Formal Verification

Bodhuin

Pagnozzi

Santone

et al. 2009

Advances in Software Engineering

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Because of its complexity, software system verification is a hard task and very often neglected for complex distributed component-based architectures with high degree of dynamism. Monitoring and verification of these systems are important even when they have to be running with a high level of availability and low halt time. Model checking is an automatic technique to verify compliance of the system implementation with respect to the requirements. In this paper we address the problem of abstracting a process model from a set of execution traces of a Java application with the aim of performing formal verification through model checking.

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Towards Faithful Model Extraction Based on Contexts

Cited by 7 publications

References 23 publications

Using contexts to extract models from code

Using contexts to extract models from code

Specification of Models Based on Contexts using Graph Grammars

Abstracting Models from Execution Traces for Performing Formal Verification

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