A Rule-based Automated Approach for Extracting Models from Source Code

Ichii, Makoto; Myojin, Tomoyuki; Nakagawa, Yuichiroh; Chikahisa, Masaki; Ogawa, Hideto

doi:10.1109/wcre.2012.40

Cited by 9 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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]. Note that, for simplification, annotations produced by the method readCmd are not included.…”

Section: Information Gatheringmentioning

confidence: 99%

“…If the annotation is related to a method, then the method name is added to the call stack and to the context trace (lines [30][31][32]. If the annotation is of the type exit-context and is related to a method, then the first method name in the call stack is removed (lines [35][36]. If the annotation is an action annotation, then the name of the action is added to the context trace (lines [38][39].…”

Section: Context Identificationmentioning

confidence: 99%

“…However, unlike the ModEx approach, our mapping from the programming language to the modelling language is predefined and automatic. In [36], the authors propose an approach similar to ModEx and to ours, where the user provides rules to translate an intermediate model, generated from the source code, to the final model. This rules are basically syntactical but incorporate the semantics of a specific domain or application to apply the appropriate abstractions.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Using contexts to extract models from code

2015

View full text Add to dashboard Cite

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.

show abstract

Section: Information Gatheringmentioning

confidence: 99%

Section: Context Identificationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Using contexts to extract models from code

2015

View full text Add to dashboard Cite

show abstract

“…In this paper, a model-checking approach for fault analysis based on a configurable model-extraction framework, named "program-oriented modeling" (POM), and a source-code MC environment, named "POM/MC" [2] are proposed. POM is a conceptual framework for analyzing the source code of target software and extracting the software model that users require.…”

Section: Introductionmentioning

confidence: 99%

A Model-Checking Approach for Fault Analysis Based on Configurable Model Extraction

Ogawa

Ichii

Myojin

et al. 2015

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

SUMMARYA practical model-checking (MC) approach for fault analysis, that is one of the most cost-effective tasks in software development, is proposed. The proposed approach is based on a technique, named "Program-oriented Modeling" (POM) for extracting a model from source code. The framework of model extraction by POM provides configurable abstraction based on user-defined transformation rules, and it supports trialand-error model extraction. An environment for MC called POM/MC was also built. POM/MC analyzes C source code to extract Promela models used for the SPIN model checker. It was applied to an industrial software system to evaluate the efficiency of the configurable model extraction by POM for fault analysis. Moreover, it was shown that the proposed MC approach can reduce the effort involved in analyzing software faults by MC.

show abstract

“…Our proposed process is implemented using a modelextraction technique called program-oriented modeling (POM) [6] [7]. The POM framework analyzes the source code of the target software, and extracts a model that users require.…”

Section: Introductionmentioning

confidence: 99%

Experimental Fault Analysis Process Implemented Using Model Extraction and Model Checking

Ogawa

Ichii

Kumeno

et al. 2015

2015 IEEE 39th Annual Computer Software and Applications Conference

Self Cite

View full text Add to dashboard Cite

When a software failure is observed during testing or operation, developers traditionally execute the software program again for reproducing the failure to analyze the cause of the failure. However, failures are often hard to reproduce because they depend on factors that are hard to expressly control, such as concurrency and nondeterminism. This paper presents a novel experimental fault analysis process for such "hard-to-reproduce failures". The proposed process consists of three phases: assumption of a hypothesis for the cause of a failure, experiments to examine the hypothesis and confirmation of the experimental results. We formalized the process and implemented it by using model extraction and model checking. Model extraction acts as a bridge between the assumption and experiment. Experiments on failure reproduction are conducted using model checking. The results of the case studies show that the process and tools supporting the process enables developers to detect the cause of hard-to-reproduce failures in industrial software development.

show abstract

A Rule-based Automated Approach for Extracting Models from Source Code

Cited by 9 publications

References 12 publications

Using contexts to extract models from code

Using contexts to extract models from code

A Model-Checking Approach for Fault Analysis Based on Configurable Model Extraction

Experimental Fault Analysis Process Implemented Using Model Extraction and Model Checking

Contact Info

Product

Resources

About