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

Abstract

Summary Model‐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.

Clone detection for business process models

“…Another threat to validity of this work is the absence of measuring the absolute recall, although we compared our technique to the state-of-the-art Apromore clone detection tool. There is no other tool against which we can compare our tool, but for a better assessment of our tool, we can follow a more automated mutation analysis ( Stephan & Cordy, 2019 ). We also applied a manual validation approach to assess the precision of the tool, which is an error-prone process as it is mainly a labor-intensive activity.…”

Metamodel clone detection with SAMOS

“…A threat to validity of this study is the lack of an assessment of the absolute recall, although we used state-of-the-art (and hence presumably reasonably accurate) clone detector tools as comparison. Ways of further mitigating this problem would be to apply a proper and automated mutation-based assessment tool [48] on the one hand, but also perform further comparative studies with other clone detectors to have a stronger account of the overall relative recall of all the tools combined. The manual labelling of clones, hence the manual validation of accuracy, is also a labor-intensive and error-prone process; to reduce the error rate, we plan to incorporate multiple assessors with techniques from the empirical research domain, and work with domain experts from the industry and the clone detection community for building ground theory [49].…”

“…Both use graph matching and graphtheoretic algorithms to find clones in sets of data flow models. ConQAT is used in comparative studies such as the one by Stephan and Cordy [48] and is reported to have lower accuracy (especially recall) than NICAD for near-miss clones. ModelCD in turn, contains two algorithms, eScan and aScan, for clone detection, however they are not publicly available.…”

AMINO: A quality assessment framework for modeling ecosystems

“…MuMonDE 53 is an MDE‐framework which applies mutation testing by randomly mutating model elements within existing projects to emulate various types of clones that can exist within that domain. This tool proposes a quality evaluation based on clone detection by offering visualizations, even though it does not offer any discovery mechanism for aggregated quality evaluations.…”