Automated provenance graphs for models@run.time

Abstract

Software systems are increasingly making decisions autonomously by incorporating AI and machine learning capabilities. These systems are known as self-adaptive and autonomous systems (SAS). Some of these decisions can have a life-changing impact on the people involved and therefore, they need to be appropriately tracked and justified: the system should not be taken as a black box. It is required to be able to have knowledge about past events and records of history of the decision making. However, tracking everything that was going on in the system at the time a decision was made may be unfeasible, due to resource constraints and complexity. In this paper, we propose an approach that combines the abstraction and reasoning support offered by models used at runtime with provenance graphs that capture the key decisions made by a system through its execution. Provenance graphs relate the entities, actors and activities that take place in the system over time, allowing for tracing the reasons why the system reached its current state. We introduce activity scopes, which highlight the high-level activities taking place for each decision, and reduce the cost of instrumenting a system to automatically produce provenance graphs of these decisions. We demonstrate a proof of concept implementation of our proposal across two case studies, and present a roadmap towards a reusable provenance layer based on the experiments.

Publication DOI: https://doi.org/10.1145/3417990.3419503
Divisions: College of Engineering & Physical Sciences > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: This is an open access article published via the read and publish agreement between Aston University and ACM 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: Autonomous decision-making,PROV-DM,Provenance,Runtime models,Self-explanation,Software,Engineering (miscellaneous)
ISBN: 978-1-4503-8135-2
Full Text Link:
Related URLs: https://dl.acm. ... 3417990.3419503 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2020-10-16
Authors: Reynolds, Owen
García-Domínguez, Antonio (ORCID Profile 0000-0002-4744-9150)
Bencomo, Nelly (ORCID Profile 0000-0001-6895-1636)

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