Difference computation of large models

Treude, Christoph; Berlik, Stefan; Wenzel, Sven; Kelter, Udo

doi:10.1145/1287624.1287665

Cited by 72 publications

(49 citation statements)

References 9 publications

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“…On the one hand, there are proposals for structured-merge tools that are specific to mainstream programming languages such as Java [2] and C++ [7]. On the other hand, there are many proposals of structured two-way and three-way merge techniques for modeling artifacts [11,15,19]-a comprehensive bibliography is available on the Web. 5 The approaches are mostly based on graphs, which allow precise merging but harm scalability.…”

Section: Related Workmentioning

confidence: 99%

Structured merge with auto-tuning: balancing precision and performance

Apel

Leßenich

Lengauer

2012

Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering

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Software-merging techniques face the challenge of finding a balance between precision and performance. In practice, developers use unstructured-merge (i.e., line-based) tools, which are fast but imprecise. In academia, many approaches incorporate information on the structure of the artifacts being merged. While this increases precision in conflict detection and resolution, it can induce severe performance penalties. Striving for a proper balance between precision and performance, we propose a structured-merge approach with auto-tuning. In a nutshell, we tune the merge process on-line by switching between unstructured and structured merge, depending on the presence of conflicts. We implemented a corresponding merge tool for Java, called JDime. Our experiments with 8 real-world Java projects, involving 72 merge scenarios with over 17 million lines of code, demonstrate that our approach indeed hits a sweet spot: While largely maintaining a precision that is superior to the one of unstructured merge, structured merge with auto-tuning is up to 12 times faster than purely structured merge, 5 times on average.

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Section: Related Workmentioning

confidence: 99%

Structured merge with auto-tuning: balancing precision and performance

Apel

Leßenich

Lengauer

2012

Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering

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“…Even for models in the same computer, OID-based identification fails if an object was deleted but then restored from scratch with a new ID, not to mention the technological difficulties of OID-based alignment. Thus, update mappings cannot be computed entirely automatically, and there are many model differencing tools [8,9,10] employing various heuristics and requiring user assistance to fix the deficiencies of the automatic identification. In general, alignment is another story, and it is useful to separate concerns: discovering updates and propagating updates are two different tasks that must be treated differently and addressed separately.…”

Section: Why State-based Synchronization Does Not Work Wellmentioning

confidence: 99%

“…An important observation is that assigning types to model elements constitutes a mapping t : A → M between two graphs underlying the model (A) and its metamodel (M ) resp. What is usually called a model graph [9,10,13] is actually an encoding of a typing mapping t. Making this mapping explicit is semantically important, especially for managing heterogeneous model mappings. Symbols in round brackets are beyond UML and say whether or not the value of the attribute may be set to Unknown (null, in the database jargon).…”

Section: Inside Models: Basics Of Meta(meta)modelingmentioning

confidence: 99%

“…usually employ complex heuristics and AI-based techniques (like, e.g., similarity flooding [17]), which are tailored to specific kinds of models or/and to specific contexts of model comparison [8,10,18]. Whatever the technic is used for model matching, the result is basically a set of matching links between the models' elements.…”

Section: Fig 9 Reification Of Matchesmentioning

confidence: 99%

“…Specifying (symmetric) deltas is a known issue, e.g., [9,10,62,63]. A major challenge is how to formally specify model changes: modifications, if we interpret deltas as updates, or conflicts, if deltas are matches.…”

Section: Concrete Model Managementmentioning

confidence: 99%

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Model Synchronization: Mappings, Tiles, and Categories

Diskin

2011

Lecture Notes in Computer Science

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Abstract. The paper presents a novel algebraic framework for specification and design of model synchronization tools. The basic premise is that synchronization procedures, and hence algebraic operations modeling them, are diagrammatic: they take a configuration (diagram) of models and mappings as their input and produce a diagram as the output. Many important synchronization scenarios are based on diagram operations of square shape. Composition of such operations amounts to their tiling, and complex synchronizers can thus be assembled by tiling together simple synchronization blocks. This gives rise to a visually suggestive yet precise notation for specifying synchronization procedures and reasoning about them.

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