Model Extraction Using Context Information

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

doi:10.1007/11880240_27

Cited by 11 publications

(24 citation statements)

References 13 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%

“…CIDs represent contexts and are mapped to states of the LKS (line 18), whereas actions are mapped to labels of the model. Whenever a CID is found in the context trace, it is checked whether it is the first context found so that variables initialState and previousState are set (lines [19][20]. If it is not the first context, it means that there has been a context found before and variable previousState contains its CID.…”

Section: Lks Constructionmentioning

confidence: 99%

“…We propose a hybrid approach for model extraction that effectively combines static and dynamic information, so that it is possible to identify how a given trace was produced in the code and support the safe merging of multiple traces. Our model extraction process is based on the concept of contexts [19], which represent abstract states of a system. Each context describes a combination of an execution point in the system control flow graph, a set of values of selected program variables (system state), and a call stack, representing the stack of method calls waiting for a return.…”

Section: Introductionmentioning

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%

See 3 more Smart Citations

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: Lks Constructionmentioning

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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“…In [8], an approach for model extraction based on the use of contexts was presented. A context represents an abstraction of a state of the system, composed of the identification of a block of code and the values of a set of attributes.…”

Section: Introductionmentioning

confidence: 99%

Towards Faithful Model Extraction Based on Contexts

Duarte

Kramer

Uchitel

Fundamental Approaches to Software Engineering

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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.

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Finite‐state model extraction and visualization from Java program execution

2020

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Finite‐state models are extensively used for discrete systems and they have also been adopted for the analysis and verification of concurrent systems. Programs that have a repetitive cycle, such as event‐driven servers and controllers, lend themselves to finite‐state modeling. In this article, we use the term model extraction to refer to the construction of a finite‐state model from an execution trace of a Java program and a set of key attributes, that is, a subset of the fields of the objects in the program execution. By choosing different sets of attributes, different finite‐state models (or views) of the execution can be obtained. Such models aid program comprehension and they can also be used in debugging a program. We present algorithms for model extraction and also for model abstraction in order to reduce the size of the extracted models so that they are amenable to visualization. For long executions, we show how to minimize the overhead of execution trace collection through a bytecode instrumentation technique; and, for large models, which are not amenable to visualization, we show how key properties of the extracted model can be checked against declarative specifications. We have implemented our techniques in the context of JIVE, an Eclipse plugin that supports runtime visualization and analysis of Java program executions. We illustrate our techniques through a collection of case studies of varying size and complexity, from classic problems of concurrency control to a medium‐size protocol for authorization (OAuth2.0 protocol) to a large‐scale software that underlies web applications (Apache Tomcat server).

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Model Extraction Using Context Information

Cited by 11 publications

References 13 publications

Using contexts to extract models from code

Using contexts to extract models from code

Towards Faithful Model Extraction Based on Contexts

Finite‐state model extraction and visualization from Java program execution

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