Operation Based Model Representation: Experiences on Inconsistency Detection

Noir, Jérôme Le; Delande, Olivier; Exertier, Daniel; Silva, Marcos Aurélio Almeida da; Blanc, Xavier

doi:10.1007/978-3-642-21470-7_7

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…With the increasing complexity and size of industrial design models, we believe that optimal memory management (scalability) in inconsistency management is as important as response time (efficiency). This is also confirmed by our industrial partners, which have realized a case study that confirms the critical need for more optimal memory management for large and complex models .…”

Section: Introductionsupporting

confidence: 73%

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Incremental inconsistency detection with low memory overhead

Falleri

Blanc

Bendraou³

et al. 2012

Softw Pract Exp

Self Cite

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Ensuring models' consistency is a key concern when using a model-based development approach. Therefore, model inconsistency detection has received significant attention over the last years. To be useful, inconsistency detection has to be sound, efficient, and scalable. Incremental detection is one way to achieve efficiency in the presence of large models. In most of the existing approaches, incrementalization is carried out at the expense of the memory consumption that becomes proportional to the model size and the number of consistency rules. In this paper, we propose a new incremental inconsistency detection approach that only consumes a small and model size-independent amount of memory. It will therefore scale better to projects using large models and many consistency rules.Many approaches dealing with inconsistency management exist in the literature. The first generation of approaches that tackle this challenge uses inconsistency rules and executes them on models in batch [9][10][11][12][13][14]. The limitation of these first generation approaches, also called batch checkers, is efficiency. The main problem is that each time a model is modified, all inconsistency rules have to be rechecked as a whole, which turns to be highly time consuming. As reported by [3], checking large models with batch checkers can then take hours to complete, which cannot be sustainable in effective development processes.A second generation of approaches has then emerged to deal with efficiency. These approaches, also called incremental checkers, limit the set of rules to recheck (rule reduction) and/or the model elements to consider (scope reduction) after each model change [15][16][17][18]. The most efficient approaches intensively use cache memory, thus introducing a memory overhead relative to the size of models and requiring a mechanism to manage the cache along the model's life cycle. An experimental study shows that one of the current fastest incremental checkers has a cache that induces a linear memory overhead on several real-world Unified Modeling Language (UML) models but that has a worst-case memory overhead that is quadratic in the number of elements [15]. With the increasing complexity and size of industrial design models, we believe that optimal memory management (scalability) in inconsistency management is as important as response time (efficiency). This is also confirmed by our industrial partners, which have realized a case study that confirms the critical need for more optimal memory management for large and complex models [19].In this paper, we present an incremental checker that puts more emphasis on scalability rather than on efficiency. It has no cache and requires only a fixed memory overhead to perform both rule and scope reductions. Even if it cannot perform reduction for some cases (explained in Section 5) and is therefore less efficient than checkers that use cache for such cases, the intensive benchmark we have performed, using real large open source models, shows that it is fast enough (rechecks are perf...

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Section: Introductionsupporting

confidence: 73%

“…Second, it presents a case study that has been performed by Thales, our industrial partner as a part of the MOVIDA project. Thales wanted to try Praxis on their domain specific models, on the basis of Eclipse Modeling Framework .…”

Section: Beyond Java: Two Praxis Case Studiesmentioning

confidence: 99%

Incremental inconsistency detection with low memory overhead

Falleri

Blanc

Bendraou³

et al. 2012

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

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“…The existing approaches typically compute a set of repairs for each of the detected inconsistencies. Studies reported on the adoption of inconsistency detection and repair in various industries, such as by Thales a company in aeronautic, transport, and security [42], and Van Hoecke Automation a company in the areas of production automation and processing [12]. In this paper, we argue that merely proposing repairs is not yet sufficient, and consequences of repairs must be handled too.…”

Section: Introductionmentioning

confidence: 88%

Detecting and exploring side effects when repairing model inconsistencies

Khelladi

Kretschmer²,

Egyed³

2019

Proceedings of the 12th ACM SIGPLAN International Conference on Software Language Engineering

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When software models change, developers often fail in keeping them consistent. Automated support in repairing inconsistencies is widely addressed. Yet, merely enumerating repairs for developers is not enough. A repair can as a side effect cause new unexpected inconsistencies (negative) or even fix other inconsistencies as well (positive). To make matters worse, repairing negative side effects can in turn cause further side effects. Current approaches do not detect and track such side effects in depth, which can increase developers' effort and time spent in repairing inconsistencies. This paper presents an automated approach for detecting and tracking the consequences of repairs, i.e. side effects. It recursively explores in depth positive and negative side effects and identifies paths and cycles of repairs. This paper further ranks repairs based on side effect knowledge so that developers may quickly find the relevant ones. Our approach and its tool implementation have been empirically assessed on 14 case studies from industry, academia, and GitHub. Results show that both positive and negative side effects occur frequently. A comparison with three versioned models showed the usefulness of our ranking strategy based on side effects. It showed that our approach's top prioritized repairs are those that developers would indeed choose. A controlled experiment with 24 participants further highlights the significant influence of side effects and of our ranking of repairs on developers. Developers who received side effect knowledge chose far more repairs with positive side effects and far less with negative side effects, while being 12.3% faster, in contrast to developers who did not receive side effect knowledge. CCS Concepts • Software and its engineering → Model-driven software engineering; Software maintenance tools.

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“…Operation-based model representation [32] has been proposed for reasoning about streams of model operations. The C-Praxis approach [35] defines six CRUD model operations and defines how these operations interact.…”

Section: Conflict-free Replicated Data Typesmentioning

confidence: 99%

Real-time Collaborative Multi-Level Modeling by Conflict-Free Replicated Data Types

Dávid¹,

Syriani²

2022

Preprint

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The need for real-time collaborative solutions in model-driven engineering has been increasing over the past years. Conflict-free replicated data types (CRDT) provide scalable and robust replication mechanisms that align well with the requirements of real-time collaborative environments. In this paper, we propose a real-time collaborative multi-level modeling framework to support advanced modeling scenarios, built on a collection of custom CRDTs, specifically tailored for the needs of modeling environments. We demonstrate the benefits of the framework through an illustrative modeling case and compare it with other state-of-the-art modeling frameworks.

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Operation Based Model Representation: Experiences on Inconsistency Detection

Cited by 8 publications

References 9 publications

Incremental inconsistency detection with low memory overhead

Incremental inconsistency detection with low memory overhead

Detecting and exploring side effects when repairing model inconsistencies

Real-time Collaborative Multi-Level Modeling by Conflict-Free Replicated Data Types

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