An approach to clone detection in behavioural models

Abstract

Abstract-In this paper we present an approach for identifying near-miss interaction clones in reverse-engineered UML behavioural models. 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 robust near-miss code clone technology, our approach is text-based, working on the level of XMI, the standard interchange serialization for UML. Behavioural model clone detection presents several challenges -first, it is not clear how to break a continuous stream of interaction between lifelines into meaningful conversational units. Second, unlike programming languages, the XMI text representation for UML is highly non-local, using attributes to reference information in the model file remotely. In this work we use a set of contextualizing source transformations on the XMI text representation to reveal the hidden hierarchical structure of the model and granularize behavioural interactions into conversational units. Then we adapt NiCad, a near-miss code clone detection tool, to help us identify conversational clones in reverse-engineered behavioural models.

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

“…MCD can applied to various kinds of models and domains, with the most mature MCD target being Simulink data-flow models [10], [13], [14]. However, recently, there has been advancements in MCD for other model types including various types of UML models [15], [16], Stateflow models [17], [18], and others. Similarly to model comparison approaches, MCD can be realized using a variety of graph-based and text-based approaches, each with their own unique advantages and disadvantages [11].…”

“…On the other hand, newer model clone detection approaches that are able to detect Type 2 (renamed) and Type 3 (nearmiss) clones [13], [15], [17], [14] are ideally suited for this task. For simpler, more purely structure-based patterns, the detected instances will be examples of Type 2 clones of the pattern, in that the structure of both the pattern model and the models being analyzed will be identical, but the element names or types will be different.…”

“…If we look at the semantics of the clones extracted, likely through the assistance of a domain expert and the techniques discussed by Al-Batran et al [32], we may be able to deduce model pattern-or antipatternlike sub graphs. A first approximation to this idea has been implemented by Antony et al [15], who used model clone detection to find instances of potential security threats in designrecovered behavioral models of web application interactions.…”

Identification of Simulink model antipattern instances using model clone detection

“…More recently, however, model clone detection approaches have been emerging for other types of models such as Stateflow models [17], [18], UML models [19], [20], and others. Some model clone detectors employ a graph-based analysis approach, while others use a text-based approach, each having their own unique benefits and shortcomings [13].…”

“…Model clone detectors capable of detecting Type 3 (nearmiss) and Type 2 (renamed) model clones [15]- [17], [19] are suitable candidates. Detection of Type 2 model clones will allow engineers to detect simple structure-based model patterns that have identical structure to, but different element names than, the systems being evaluated.…”