SiMPOSE - Configurable N-Way Program Merging Strategies for Superimposition-Based Analysis of Variant-Rich Software

Reuling, Dennis; Kelter, Udo; Ruland, Sebastian; Lochau, Malte

doi:10.1109/ase.2019.00120

Cited by 2 publications

(3 citation statements)

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“…Several approaches which can be characterized as merge refactoring have been proposed in the context of migrating a set of variants into an integrated software product line. Starting from a set of "anchor points" which indicate corresponding elements, the key idea is to extract the common parts in a step-wise manner through a series of variantpreserving refactorings [29,43,51,68,[71][72][73][74][75][76]. Anchor points may be determined through clone detection [43,[72][73][74] or conventional matchers [29,51,71,75,76], and may be corrected and improved by the merge refactoring.…”

Section: Related Workmentioning

confidence: 99%

“…Starting from a set of "anchor points" which indicate corresponding elements, the key idea is to extract the common parts in a step-wise manner through a series of variantpreserving refactorings [29,43,51,68,[71][72][73][74][75][76]. Anchor points may be determined through clone detection [43,[72][73][74] or conventional matchers [29,51,71,75,76], and may be corrected and improved by the merge refactoring. However, such implicit calculations of optimized n-way matchings require extensive catalogues of language-specific refactoring operations which have to be specified manually [51,[73][74][75].…”

Section: Related Workmentioning

confidence: 99%

“…The order in which input models are processed influences the quality of the matching because better match candidates may be found after an element has already been matched. An order might be determinable if a reference model is given, but this is typically not the case [9,22,[29][30][31][32][33]]. An optimal processing order cannot be anticipated and applying all n!…”

Section: Introductionmentioning

confidence: 99%

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RaQuN: a generic and scalable n-way model matching algorithm

et al. 2022

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Model matching algorithms are used to identify common elements in input models, which is a fundamental precondition for many software engineering tasks, such as merging software variants or views. If there are multiple input models, an n-way matching algorithm that simultaneously processes all models typically produces better results than the sequential application of two-way matching algorithms. However, existing algorithms for n-way matching do not scale well, as the computational effort grows fast in the number of models and their size. We propose a scalable n-way model matching algorithm, which uses multi-dimensional search trees for efficiently finding suitable match candidates through range queries. We implemented our generic algorithm named RaQuN (Range Queries on $$\text {N}$$ N input models) in Java and empirically evaluate the matching quality and runtime performance on several datasets of different origins and model types. Compared to the state of the art, our experimental results show a performance improvement by an order of magnitude, while delivering matching results of better quality.

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