Cronista:A multi-database automated provenance collection system for runtime-models

Abstract

Context: Decision making by software systems that face uncertainty needs tracing to support understandability, as accountability is crucial. While logging has been essential to support explanations and understandability of behaviour, several issues still persist, such as the high cost for managing large logs, not knowing what to log, and the inability of logging techniques to relate events to each other or to specific occurrences of high-level activities in the system. Objective: Cronista is an alternative to logging for systems that act on top of runtime models. Instead of targeting the running systems, Cronista automatically collects the provenance of changes made to the runtime models, which aim at leveraging high-level representations, to produce more concise historical records. The provenance graphs capture causal links between those changes and the activities of the system, which are used to investigate issues. Method: Cronista’s architecture is described with the current design and the implementation of its high-level components for single-machine, multi-threaded systems. Cronista is applied to a traffic-simulation case study. The trade-offs of two different storage solutions are evaluated, i.e. the CDO model repositories, and JanusGraph graph databases. Results: Integrating Cronista into the case study requires only minor code changes. Cronista collected provenance graphs for the simulations as they ran, using both CDO and JanusGraph. Both solutions highlighted the cause of a seeded defect in the system. For the longer executions, both CDO and JanusGraph showed negligible overhead on the simulation times. Querying and visualisation tools were more user-friendly in JanusGraph than in CDO. Conclusion: Cronista demonstrates the feasibility of recording fine-grained provenance for the evolution of runtime models, while using it to investigate issues. User convenience and resource requirements need to be balanced. The paper present how the available technologies studied offer different trade-offs to satisfy the balance required.

Publication DOI: https://doi.org/10.1016/j.infsof.2021.106694
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
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Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0 Funding: The work was partially funded by the Leverhulme Trust Research Fellowship (Grant RF-2019-548) and the EPSRC Research Project Twenty20Insight (Grant EP/T017627/1).
Uncontrolled Keywords: Multi-threading,Provenance,Runtime-models,Self-adaptation,Self-explanation,Software,Information Systems,Computer Science Applications
Publication ISSN: 1873-6025
Last Modified: 11 Jun 2024 16:44
Date Deposited: 10 Aug 2021 08:18
Full Text Link:
Related URLs: https://linking ... 95058492100149X (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-01
Published Online Date: 2021-08-08
Accepted Date: 2021-07-20
Authors: Reynolds, Owen
García-Domínguez, Antonio (ORCID Profile 0000-0002-4744-9150)
Bencomo, Nelly (ORCID Profile 0000-0001-6895-1636)

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