A Rule-based Method to Match Software Patterns Against UML Models

Ballis, Demis; Baruzzo, Andrea; Comini, Marco

doi:10.1016/j.entcs.2008.10.034

Cited by 16 publications

(6 citation statements)

References 2 publications

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“…Another issue to those approaches is the choice of thresholds whose definition is difficult. In the same way, using the approach proposed by Ballis et al [15], [16] requires also to learn the proposed language and to be able to describe structurally and semantically anti-patterns using that language. Thus all the above approaches are concerned with the first limitation.…”

Section: Related Workmentioning

confidence: 99%

“…Rahma et al [14] used quality metrics to identify and predict anti-patterns in UML designs using structural and behavioral data. Ballis et al [15], [16] worked on both the detection of design patterns and antipatterns using a rule-based matching approach for extracting all occurrences of a design pattern/anti-pattern in a graph representation of the source code. Langelier et al [17] proposed a visual approach to detect anti-patterns.…”

Section: Related Workmentioning

confidence: 99%

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SMURF: A SVM-based Incremental Anti-pattern Detection Approach

Maïga

Ali

Bhattacharya

et al. 2012

2012 19th Working Conference on Reverse Engineering

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Abstract-In current, typical software development projects, hundreds of developers work asynchronously in space and time and may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and-or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns incrementally and on subsets of a system could reduce costs, effort, and resources by allowing practitioners to identify and take into account occurrences of anti-patterns as they find them during their development and maintenance activities. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently four limitations: (1) they require extensive knowledge of anti-patterns, (2) they have limited precision and recall, (3) they are not incremental, and (4) they cannot be applied on subsets of systems. To overcome these limitations, we introduce SMURF, a novel approach to detect anti-patterns, based on a machine learning technique-support vector machines-and taking into account practitioners' feedback. Indeed, through an empirical study involving three systems and four anti-patterns, we showed that the accuracy of SMURF is greater than that of DETEX and BDTEX when detecting anti-patterns occurrences. We also showed that SMURF can be applied in both intra-system and inter-system configurations. Finally, we reported that SMURF accuracy improves when using practitioners' feedback.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

SMURF: A SVM-based Incremental Anti-pattern Detection Approach

Maïga

Ali

Bhattacharya

et al. 2012

2012 19th Working Conference on Reverse Engineering

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“…What we propose, instead, is to exploit a formal definition of Patterns in order to write agnostic, general rules of recognition, not bound to the specific Pattern to recognize. Another interesting approach is the one described in [5]: in this work the authors define an extremely precise, rigorous and formal language to describe Design patterns and an algorithm, based on rules which exploit the defined formalism to recognize them. What is arguable in this approach is that UML diagrams should be expressed in the same formal language as the Design patterns for the recognition algorithm to work, but the authors don't explain how this can be done.…”

Section: State Of the Artmentioning

confidence: 99%

Automatic Recognition of Design Patterns from UML-based Software Documentation

Martino

Esposito

2013

Proceedings of International Conference on Information Integration and Web-Based Applications &Amp; Services

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Here we describe a procedure and a prototype implementation for the automatic recognition of Design Patterns from documentation of Software Artefacts' design and implementation, provided in a machine readable form, namely the XMI coded representation of UML diagrams. The procedure exploits a semantic representation of the patterns to be recognized, based on the ODOL ontology defined by the University of Massey (New Zealand) [12], which we have augmented with an OWL-S based representation of the patterns' dynamic behaviour. Both the UML set of diagrams related to the analysed Software Artefacts and the ODOL+OWL-S patterns' representation are automatically scanned and translated into a first order logic representation (namely Prolog). A set of first order logic rules, independent from the specific pattern to be recognized, have been defined to describe the heuristics and features which trigger the recognition, exploiting the Prolog description of the patterns to be recognized and the base of Prolog facts which represents the UML documentation.

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“…They are typically based on pragmatic experience and involve descriptions of when and where the pattern/antipattern should be applied, implementation variants, and justification. Often these patterns have an abstract representation in model form [6], however, detection is generally accomplished by evaluating code [7] or, in some cases, by writing complex textual rules to evaluate models [8].…”

Section: Introductionmentioning

confidence: 99%

Identifying Instances of Model Design Patterns and Antipatterns Using Model Clone Detection

Stephan

Cordy

2015

2015 IEEE/ACM 7th International Workshop on Modeling in Software Engineering

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Abstract-A hurdle in the growth of model driven software engineering is our ability to evaluate the quality of models automatically. One perspective is that software quality is a function of the existence, or lack thereof, of good and bad properties, also known as patterns and antipatterns, respectively. In this paper, we introduce the notion of using model clone detection to detect model pattern and antipattern instances by looking for models that are cross clones of pattern models. By detecting patterns at the model level, analysis is accomplished earlier in the engineering process, can be applied to primarily modelbased projects, and remains at the same level of abstraction that engineers are used to. We outline the process of using model clone detection for this purpose, including representing the patterns and detection of instances. We present some Simulink examples of pattern representations and discuss future work and research in the area. I. INTRODUCTIONModel-driven Engineering (MDE) is a relatively new approach to software development that entails higher-level abstractions, or models, being used as the primary artifacts in system development and management. This includes incorporating modeling in all four phases of Software Engineering: requirements, design, implementation, and testing. By utilizing artifacts at an abstraction level closer to the problem space and hiding lower-level details, MDE can yield higher quality systems, facilitate better communication among stakeholders, and make project teams adaptable and flexible. MDE has seen significant levels of adoption in many different domains, including aviation, automotive, aerospace, and other highreliability embedded systems applications.While MDE use is growing, and engineers and customers are starting to experience the benefits of using model-based techniques [1], there are still many obstacles to overcome. One example is model quality evaluation, that is, the ability to assess quality of the models and artifacts of interest across the MDE life cycle [2], [3]. When it comes to more traditional software engineering processes, such as those that center around third generation programming languages, quality assurance is a well-researched and established area. In contrast, much less is understood about quality assurance for models in the MDE context. Improving our knowledge and making it easier to reason about model quality is a necessary step in continuing the growth of MDE.One approach to assessing software quality involves detecting the existence of design patterns [4] and antipatterns [5] in

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A Rule-based Method to Match Software Patterns Against UML Models

Cited by 16 publications

References 2 publications

SMURF: A SVM-based Incremental Anti-pattern Detection Approach

SMURF: A SVM-based Incremental Anti-pattern Detection Approach

Automatic Recognition of Design Patterns from UML-based Software Documentation

Identifying Instances of Model Design Patterns and Antipatterns Using Model Clone Detection

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