TY - GEN
T1 - Automated provenance graphs for models@run.time
AU - Reynolds, Owen
AU - García-Domínguez, Antonio
AU - Bencomo, Nelly
N1 - This is an open access article published via the read and publish agreement between Aston University and ACM
PY - 2020/10/16
Y1 - 2020/10/16
N2 - 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.
AB - 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.
KW - Autonomous decision-making
KW - PROV-DM
KW - Provenance
KW - Runtime models
KW - Self-explanation
UR - https://dl.acm.org/doi/10.1145/3417990.3419503
UR - http://www.scopus.com/inward/record.url?scp=85096778397&partnerID=8YFLogxK
U2 - 10.1145/3417990.3419503
DO - 10.1145/3417990.3419503
M3 - Conference publication
T3 - Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings
SP - 344
EP - 353
BT - Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings
PB - ACM
ER -