In highly dynamic environments, systems are expected to make decisions on the fly based on their observations that are bound to be partial. As such, the reasons for its runtime behaviour may be difficult to understand. In these cases, accountability is crucial, and decisions by the system need to be traceable. Logging is essential to support explanations of behaviour, but it poses challenges. Concerns about analysing massive logs have motivated the introduction of structured logging, however, knowing what to log and which details to include is still a challenge. Structured logs still do not necessarily relate events to each other, or indicate time intervals. We argue that logging changes to a runtime model in a provenance graph can mitigate some of these problems. The runtime model keeps only relevant details, therefore reducing the volume of the logs, while the provenance graph records causal connections between the changes and the activities performed by the agents in the system that have introduced them. In this paper, we demonstrate a first version towards a reusable infrastructure for the automated construction of such a provenance graph. We apply it to a multithreaded traffic simulation case study, with multiple concurrent agents managing different parts of the simulation. We show how the provenance graphs can support validating the system behaviour, and how a seeded fault is reflected in the provenance graphs. CCS CONCEPTS • Software and its engineering → System modeling languages; Integration frameworks; Model-driven software engineering.

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. CCS CONCEPTS • Software and its engineering → Abstraction, modeling and modularity; • Computing methodologies → Model development and analysis; • Applied computing → Evidence collection, storage and analysis.

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<p>Evaluating the quality of data is a major concern within the scientific community: before using any dataset for study, a careful judgement of its suitability must be conducted. This requires that the steps followed to acquire, select, and process the data have been thoroughly documented in a methodical manner, in a way that can be clearly communicated to the rest of the community. This is particularly important in the field of citizen science, where a project that can clearly demonstrate its protocols, transformation steps, and quality assurance procedures have much more chance of achieving social and scientific impact through the use and re-use of its data.</p><p>A number of specifications have been created to provide a common set of concepts and terminology, such as ISO 19115-3 or W3C PROV. These define a set of interchange formats, but in themselves, they do not provide tooling to create high-quality dataset descriptions. The existing tools built on these standards (e.g. GeoNetwork, USGS metadata wizard, CKAN) are overly complex for some users (for example, many citizen science project managers) who, despite being experts in their own fields, may be unfamiliar with the structure and context of metadata standards or with semantic modelling. </p><p>In this presentation, we will describe a prototype authoring tool that was created using a Model-driven engineering (MDE) software development methodology. The tool was authored using JetBrains Meta Programming System (MPS) to implement a modelling language based on the ISO19115-3 model. A user is provided with a “text-like” editing environment, which assists with the formal structures needed to produce a machine-parable document.</p><p>This allows a user to easily describe data lineage and generic processing steps while reusing recognised external vocabularies with automated validation, autocompletion, and transformation to external formats (e.g. the XML format 19115-3 or JSON-LD). We will report on the results of user testing aimed at making the tool accessible to citizen scientists (through dedicated projections with simplified structures and dialogue-driven model creation) and evaluating with those users any new possibilities that comprehensive and machine-parsable provenance information may create for data integration and sharing. The prototype will also serve as a test pilot of the integration between ISO 19115-3 and existing/upcoming third-party vocabularies (such as the upcoming ISO data quality measures registry).</p>