Model Clone Detection Research Articles

A methodology for model clone detection using statistics of design metrics

Model clone detection has predominantly gained momentum in the field of software development process and model driven engineering. Reuse and mutations of the existing models will increase the complexity of managing the software’s organisation’s internal repositories. Detecting semantic and structural similarities among the models finds various uses like fault prediction, estimation of maintenance and refactoring. Literature in the domain witnesses very few works focussing on model clone detection. This work proposes a model clone detection technique by leveraging the statistical and lexical properties of the UML diagrams. The primary contribution of the work is the construction of Similarity Measure (SM) by analysing the design metrics from different perspectives namelyi)measuring the statistical variability between the models and ii) estimating the lexical similarity among design metrics. The results of the model indicates that the proposed method was able to detect the semantically similar model clones of the banking use case. Also, the method is very robust and computationally inexpensive, so that it could find its applicability in all fields where model driven engineering is used.

Clone detection for business process models

Models are key in software engineering, especially with the rise of model-driven software engineering. One such use of modeling is in business process modeling, where models are used to represent processes in enterprises. As the number of these process models grow in repositories, it leads to an increasing management and maintenance cost. Clone detection is a means that may provide various benefits such as repository management, data prepossessing, filtering, refactoring, and process family detection. In model clone detection, highly similar model fragments are mined from larger model repositories. In this study, we have extended SAMOS (Statistical Analysis of Models) framework for clone detection of business process models. The framework has been developed to support different types of analytics on models, including clone detection. We present the underlying techniques utilized in the framework, as well as our approach in extending the framework. We perform three experimental evaluations to demonstrate the effectiveness of our approach. We first compare our tool against the Apromore toolset for a pairwise model similarity using a synthetic model mutation dataset. As indicated by the results, SAMOS seems to outperform Apromore in the coverage of the metrics in pairwise similarity of models. Later, we do a comparative analysis of the tools on model clone detection using a dataset derived from the SAP Reference Model Collection. In this case, the results show a better precision for Apromore, while a higher recall measure for SAMOS. Finally, we show the additional capabilities of our approach for different model scoping styles through another set of experimental evaluations.

A Process Model Collection with Structural Variants and Evaluations

Today in the era of the latest technologies, Business Process Management Systems (BPMS) have allowed organizations to build process model repositories which help to maintain the flow of operations in the form of various process models. Business process models are virtual models that can imitate the actual activities of an organization. Searching for semantically similar activities between pairs of process models in a repository is known as Process Model Matching (PMM). From the past few years, PMM has been gaining momentum due to its wide range of applications such as integration of process models, process model clone detection, and process model knowledge discovery. Different types of PMM techniques have been applied on available process model repositories but these repositories contained a limited number of process models. Another notable aspect of PMM is that the existing techniques have not achieved the desired results which questions the effectiveness of process model repositories. To address this problem, the authors of this study have developed a substantial, diverse, and carefully developed process model collection. This process model collection is compared with existing SAP collection to highlight its significance and superiority. Furthermore, the proposed process model collection represents structural variations of example process models which are governed by the defined set of rules. To reflect structural variations between process models of our collection, existing structural similarity approaches such as structural metrics and graph edit distance were applied by using a custom-developed tool. Our proposed process model collection is freely available to the research community which can be used to build new PMM techniques and for assessment of existing PMM techniques.

Analysis and Prioritization of Design Metrics

Clones at domain level during the design phase plays an important role to identify the risk at an early stage. The authors have analyzed the Design Metrics (DM) for its contribution towards the structural properties in domain models of the software. Then prioritization of these DM leads to classification of the majorly contributing DM elements. These Design Metrics relates to classes within the class diagram at design level that is useful for the identification of cross cutting concerns, origin analysis of software, software quality prediction and maintenance at the class level granularity. The authors have empirically evaluated the results for structural model clone detection.

Metamodel clone detection with SAMOS

Abstract Wider adoption of model-driven engineering leads to an abundance of models and metamodels in academic and industrial practice. One of the key techniques for the management and maintenance of such artifacts is model clone detection, where highly similar (meta-)models and (meta-)model fragments are mined from a typically large set of data. In this paper we have extended the SAMOS framework (Statistical Analysis of MOdelS) to clone detection, exemplified on Ecore metamodels. Our clone detection approach uses and extends the framework’s feature extraction, vector space model, natural language processing and clustering capabilities. We performed three extensive case studies to demonstrate its accuracy both quantitatively and qualitatively. We first compared the sensitivity and accuracy of SAMOS for metamodel changes through mutation and scenario analysis (which simulate clones) with those of NICAD-Ecore and MACH, tools for clone detection on Ecore and UML models respectively. We then compared the precision and recall of SAMOS and of NICAD-Ecore on a real dataset, consisting of conference management metamodels from the ATL Zoo. Finally we performed a repository-wide mining of metamodel clones from GitHub. We conclude that SAMOS stands out with its higher accuracy and yet considerable scalability for further large-scale clone detection and other empirical studies on metamodels and domain specific languages.

MuMonDE: A framework for evaluating model clone detectors using model mutation analysis

SummaryModel‐driven engineering is an increasingly prevalent approach in software engineering where models are the primary artifacts throughout a project's life cycle. A growing form of analysis and quality assurance in these projects is model clone detection, which identifies similar model elements. As model clone detection research and tools emerge, methods must be established to assess model clone detectors and techniques. In this paper, we describe the MuMonDE framework, which researchers and practitioners can use to evaluate model clone detectors using mutation analysis on the models each detector is geared towards. MuMonDE applies mutation testing in a novel way by randomly mutating model elements within existing projects to emulate various types of clones that can exist within that domain. It consists of 2 main phases. The mutation phase involves determining the mutation targets, selecting the appropriate mutation operations, and injecting mutants. The second phase, evaluation, involves detecting model clones, preprocessing clone reports, analyzing those reports to calculate recall and precision, and visualizing the data. We introduce MuMonDE by describing each phase in detail. We present our experiences and examples in successfully developing a MuMonDE implementation capable of evaluating Simulink model clone detectors. We validate MuMonDE by demonstrating its ability to answer evaluation questions and provide insights based on the data it generates. With this research using mutation analysis, our goal is to improve model clone detection and its analytical capabilities, thus improving model‐driven engineering as a whole.

Model clone detection for rule-based model transformation languages

Cloning is a convenient mechanism to enable reuse across and within software artifacts. On the downside, it is also a practice related to severe long-term maintainability impediments, thus generating a need to identify clones in affected artifacts. A large variety of clone detection techniques have been proposed for programming and modeling languages; yet no specific ones have emerged for model transformation languages. In this paper, we explore clone detection for rule-based model transformation languages, including graph-based ones, such as Henshin, and hybrid ones, such as ATL. We introduce use cases for such techniques in the context of constructive and analytical quality assurance, and a set of key requirements we derived from these use cases. To address these requirements, we describe our customization of existing model clone detection techniques: We consider eScan, an a-priori-based technique, ConQAT, a heuristic technique, and a hybrid technique based on a combination of eScan and ConQAT. We compare these techniques in a comprehensive experimental evaluation, based on three realistic Henshin rule sets, and a comprehensive body of examples from the ATL transformation zoo. Our results indicate that our customization of ConQAT enables the efficient detection of the considered clones, without sacrificing accuracy. With our contributions, we present the first evidence on the usefulness of model clone detection for the quality assurance of model transformations and pave the way for future research efforts at the intersection of model clone detection and model transformation.

An approach to clone detection in sequence diagrams and its application to security analysis

Duplication in software systems is an important issue in software quality assurance. While many methods for software clone detection in source code and structural models have been described in the literature, little has been done on similarity in the dynamic behaviour of interactive systems. In this paper, we present an approach to identifying near-miss interaction clones in reverse-engineered UML sequence diagrams. Our goal is to identify patterns of interaction (“conversations”) that can be used to characterize and abstract the run-time behaviour of web applications and other interactive systems. In order to leverage existing robust near-miss code clone technology, our approach is text-based, working on the level of XMI, the standard interchange serialization for UML. Clone detection in UML behavioural models, such as sequence diagrams, presents a number of challenges—first, it is not clear how to break a continuous stream of interaction between lifelines (representing the objects or actors in the system) into meaningful conversational units. Second, unlike programming languages, the XMI text representation for UML is highly non-local, using attributes to reference-related elements in the model file remotely. In this work, we use a set of contextualizing source transformations on the XMI text representation to localize related elements, exposing the hidden hierarchical structure of the model and allowing us to granularize behavioural interactions into conversational units. Then we adapt NICAD, a robust near-miss code clone detection tool, to help us identify conversational clones in reverse-engineered behavioural models. These conversational clones are then analysed to find worrisome interactions that may indicate security access violations.

Detecting approximate clones in business process model repositories

Empirical evidence shows that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication arises for example when the repository covers multiple variants of the same processes or due to copy-pasting. Previous work has addressed the problem of efficiently retrieving exact clones that can be refactored into shared subprocess models. This paper studies the broader problem of approximate clone detection in process models. The paper proposes techniques for detecting clusters of approximate clones based on two well-known clustering algorithms: DBSCAN and Hierarchical Agglomerative Clustering (HAC). The paper also defines a measure of standardizability of an approximate clone cluster, meaning the potential benefit of replacing the approximate clones with a single standardized subprocess. Experiments show that both techniques, in conjunction with the proposed standardizability measure, accurately retrieve clusters of approximate clones that originate from copy-pasting followed by independent modifications to the copied fragments. Additional experiments show that both techniques produce clusters that match those produced by human subjects and that are perceived to be standardizable.

Clone detection in UML class models using class metrics

This paper presents a technique to detect clones in UML class models. Class metrics (number of attributes, number of operations) of a class, class attribute names, root nodes, child nodes and class method names are compared with corresponding metrics, attribute names, root node, child nodes and method names of another class. Based on the number of matched attributes, operations and metrics, a percentage of cloning is calculated. We declare two classes as clones of each other if the matching percentage of the class metrics, class attribute names and class method names is greater than a specific threshold.

A Completeness Optimized Algorithm for Closed Model Clone Detection

The detection of model clone has been an active research area in recent years. The closed clone instances contain all the information of model clones so they can ensure the completeness of detection results essentially. In order to improve the degree of completeness in clone detection, a novel model clone detection algorithm named CL_MCD (Closed Model Clone Detection) is proposed. CL_MCD focuses on exactly matched clones and aims to find all the closed clone instances. The main innovation of CL_MCD is in the detection phase. Every time after finding a new node pair with the same label in the breadth-first search of model graph, CL_MCD transforms all the node pairs into a clone pair, and puts the clone pair into a set that contains all the candidate clone instances if its size is greater than or equal to the size of minimum clone. Then every candidate clone instance is compared with all the others in the set. If a candidate clone instance is one part of any other instance, it is deleted. After the filtering, redundant clone instances are removed and only the closed clone instances are kept in the set. Theoretical analysis and experimental studies demonstrate that CL_MCD has higher degree of completeness than CloneDetective.

A Novel Optimized Path-Based Algorithm for Model Clone Detection

According to previous reports software clones are considered harmful for software maintenance. Likewise, model clones are problematic in model-based development. It is significant to detect model clones in software models. In this paper, we present a novel optimized path-based model clone detection algorithm (OPMCD). It first builds paths from block graphs, and then identifies clone instances from the common subsequence of paths. Moreover, an experiment is designed to evaluate the algorithm through comparing with the state-of-the-art of model clone detection algorithm ConQAT model clone detection (CMCD). The experiment result illustrates that OPMCD has better performance in terms of efficiency, and it is practically suitable for large-scale MATLAB/Simulink models.

Towards clone detection in UML domain models

Code clones (i.e., duplicate fragments of code) have been studied for long, and there is strong evidence that they are a major source of software faults. Anecdotal evidence suggests that this phenomenon occurs similarly in models, suggesting that clones are as detrimental to quality as they are to code quality. However, programming language code and visual models have significant differences that make it difficult to directly transfer notions and algorithms developed in the code arena to clones. In this article, we develop and propose a definition of the notion of model clone based on the thorough analysis of practical scenarios. We propose a formal definition of clones, specify a detection algorithm for UML domain models, and implement it prototypically. We investigate different similarity heuristics to be used in the algorithm, and report the performance of our approach. While we believe that our approach advances the state of the art significantly, it is restricted to UML models, its results leave room for improvements, and there is no validation by field studies.