首页> 外文期刊>Software and systems modeling >The Train Benchmark: cross-technology performance evaluation of continuous model queries
【24h】

The Train Benchmark: cross-technology performance evaluation of continuous model queries

机译:培训基准:连续模型查询的跨技术性能评估

获取原文
获取原文并翻译 | 示例

摘要

In model-driven development of safety-critical systems (like automotive, avionics or railways), well-formedness of models is repeatedly validated in order to detect design flaws as early as possible. In many industrial tools, validation rules are still often implemented by a large amount of imperative model traversal code which makes those rule implementations complicated and hard to maintain. Additionally, as models are rapidly increasing in size and complexity, efficient execution of validation rules is challenging for the currently available tools. Checking well-formedness constraints can be captured by declarative queries over graph models, while model update operations can be specified as model transformations. This paper presents a benchmark for systematically assessing the scalability of validating and revalidating well-formedness constraints over large graph models. The benchmark defines well-formedness validation scenarios in the railway domain: a metamodel, an instance model generator and a set of well-formedness constraints captured by queries, fault injection and repair operations (imitating the work of systems engineers by model transformations). The benchmark focuses on the performance of query evaluation, i.e. its execution time and memory consumption, with a particular emphasis on reevaluation. We demonstrate that the benchmark can be adopted to various technologies and query engines, including modeling tools; relational, graph and semantic databases. The Train Benchmark is available as an open-source project with continuous builds from https://github.com/FTSRG/trainbenchmark .
机译:在模型驱动的安全关键系统(例如汽车,航空电子设备或铁路)的开发中,反复验证模型的格式正确性,以便尽早发现设计缺陷。在许多工业工具中,验证规则仍然经常通过大量的命令式遍历代码来实现,这使那些规则的实现变得复杂且难以维护。另外,由于模型的大小和复杂性正在迅速增加,因此对于当前可用的工具而言,有效执行验证规则具有挑战性。可以通过对图模型的声明性查询来捕获检查格式正确性约束的方法,而可以将模型更新操作指定为模型转换。本文提出了一个基准,用于系统地评估在大型图模型上验证和重新验证格式正确性约束的可伸缩性。该基准定义了铁路领域中的良好性验证场景:一个元模型,一个实例模型生成器以及一组通过查询,故障注入和修复操作(通过模型转换来模仿系统工程师的工作)捕获的良好性约束。该基准测试专注于查询评估的性能,即其执行时间和内存消耗,特别着重于评估。我们证明了基准可以被各种技术和查询引擎采用,包括建模工具。关系,图和语义数据库。可以从https://github.com/FTSRG/trainbenchmark进行连续构建,将“培训基准”作为一个开源项目获得。

著录项

  • 来源
    《Software and systems modeling》 |2018年第4期|1365-1393|共29页
  • 作者单位

    Department of Measurement and Information Systems, Budapest University of Technology and Economics,MTA-BME Lendület Research Group on Cyber-Physical Systems,Department of Electrical and Computer Engineering, McGill University;

    Department of Measurement and Information Systems, Budapest University of Technology and Economics;

    Department of Measurement and Information Systems, Budapest University of Technology and Economics,IncQuery Labs Ltd., Bocskai út 77–79;

    Department of Measurement and Information Systems, Budapest University of Technology and Economics,MTA-BME Lendület Research Group on Cyber-Physical Systems,Department of Electrical and Computer Engineering, McGill University;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Well-formedness validation; Query evaluation; Performance benchmark; Graph databases; Semantic databases; Relational databases;

    机译:格式正确性验证;查询评估;性能基准;图形数据库;语义数据库;关系数据库;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号