Partial Loading of Repository-Based Models through Static Analysis

Sorour Jahanbin; Dimitris Kolovos; Simos Gerasimou; Gerson Sunyé

doi:10.1145/3567512.3567535

Partial Loading of Repository-Based Models through Static Analysis

Sorour Jahanbin, Dimitris Kolovos, Simos Gerasimou, Gerson Sunyé

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

As the size of software and system models grows, scalability issues in the current generation of model management languages (e.g. transformation, validation) and their supporting tooling become more prominent. To address this challenge, execution engines of model management programs need to become more efficient in their use of system resources. This paper presents an approach for partial loading of large models that reside in graph-database-backed model repositories. This approach leverages sophisticated static analysis of model management programs and auto-generation of graph (Cypher) queries to load only relevant model elements instead of naively loading the entire models into memory. Our experimental evaluation shows that our approach enables model management programs to process larger models, faster, and with a reduced memory footprint compared to the state of the art.

Original language	English
Title of host publication	Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022)
Publisher	ACM
Pages	266–278
Number of pages	13
DOIs	https://doi.org/10.1145/3567512.3567535
Publication status	Published - 1 Dec 2022
Event	Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022) - University of Auckland, Auckland, New Zealand Duration: 6 Nov 2022 → 7 Nov 2022 https://2022.splashcon.org/home/sle-2022

Conference

Conference	Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022)
Country/Territory	New Zealand
City	Auckland
Period	6/11/22 → 7/11/22
Internet address	https://2022.splashcon.org/home/sle-2022

Bibliographical note

© 2022 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Access to Document

10.1145/3567512.3567535

Partial Loading of Repository-based Models through Static Analysis
© 2022 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details
Accepted author manuscript, 920 KB

Cite this

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title = "Partial Loading of Repository-Based Models through Static Analysis",

abstract = "As the size of software and system models grows, scalability issues in the current generation of model management languages (e.g. transformation, validation) and their supporting tooling become more prominent. To address this challenge, execution engines of model management programs need to become more efficient in their use of system resources. This paper presents an approach for partial loading of large models that reside in graph-database-backed model repositories. This approach leverages sophisticated static analysis of model management programs and auto-generation of graph (Cypher) queries to load only relevant model elements instead of naively loading the entire models into memory. Our experimental evaluation shows that our approach enables model management programs to process larger models, faster, and with a reduced memory footprint compared to the state of the art.",

author = "Sorour Jahanbin and Dimitris Kolovos and Simos Gerasimou and Gerson Suny{\'e}",

note = "{\textcopyright} 2022 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details ; Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022) ; Conference date: 06-11-2022 Through 07-11-2022",

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month = dec,

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doi = "10.1145/3567512.3567535",

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publisher = "ACM",

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Jahanbin, S , Kolovos, D , Gerasimou, S & Sunyé, G 2022, Partial Loading of Repository-Based Models through Static Analysis. in Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022). ACM, pp. 266–278, Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering (SLE 2022), Auckland, New Zealand, 6/11/22. https://doi.org/10.1145/3567512.3567535

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N2 - As the size of software and system models grows, scalability issues in the current generation of model management languages (e.g. transformation, validation) and their supporting tooling become more prominent. To address this challenge, execution engines of model management programs need to become more efficient in their use of system resources. This paper presents an approach for partial loading of large models that reside in graph-database-backed model repositories. This approach leverages sophisticated static analysis of model management programs and auto-generation of graph (Cypher) queries to load only relevant model elements instead of naively loading the entire models into memory. Our experimental evaluation shows that our approach enables model management programs to process larger models, faster, and with a reduced memory footprint compared to the state of the art.

AB - As the size of software and system models grows, scalability issues in the current generation of model management languages (e.g. transformation, validation) and their supporting tooling become more prominent. To address this challenge, execution engines of model management programs need to become more efficient in their use of system resources. This paper presents an approach for partial loading of large models that reside in graph-database-backed model repositories. This approach leverages sophisticated static analysis of model management programs and auto-generation of graph (Cypher) queries to load only relevant model elements instead of naively loading the entire models into memory. Our experimental evaluation shows that our approach enables model management programs to process larger models, faster, and with a reduced memory footprint compared to the state of the art.

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