From Failure to Alignment: A Requirements Engineering Framework for Machine Learning Systems

Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of users, engineers, or wider society. By focusing on stakeholders, Requirements Engineering is well positioned to drive the design and engineering of MLS that align with the needs of their stakeholders. Yet, we still need a systematic process for modelling and reasoning about requirements for MLS that is driven both by stakeholders' needs and constraints for MLS development. This paper proposes a framework entitled REAL (Requirements Engineering for mAchines that Learn - and Fail) to help develop MLS that align with stakeholders' needs by adopting a requirements engineering approach. This model-based framework is based on three principles. First, weaving together requirements for data, models, and the system as a whole. Second, using failure to drive the exploration of alternative requirements. Third, iterative and traceable refinement of MLS requirements. We demonstrate the proposed framework using an example from autonomous driving and show that REAL supports the development of MLS that better align with stakeholders' requirements. A replication package is available online.

On Specifying for Trustworthiness

As autonomous systems are becoming part of our daily lives, ensuring their trustworthiness is crucial. There are a number of techniques for demonstrating trustworthiness. Common to all these techniques is the need to articulate specifications. In this paper, we take a broad view of specification, concentrating on top-level requirements including but not limited to functionality, safety, security and other non-functional properties. The main contribution of this article is a set of high-level intellectual challenges for the autonomous systems community related to specifying for trustworthiness. We also describe unique specification challenges concerning a number of application domains for autonomous systems.

Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems

Many software systems have become too large and complex to be managed efficiently by human administrators, particularly when they operate in uncertain and dynamic environments and require frequent changes. Requirements-driven adaptation techniques have been proposed to endow systems with the necessary means to autonomously decide ways to satisfy their requirements. However, many current approaches rely on general-purpose languages, models and/or frameworks to design, develop and analyze autonomous systems. Unfortunately, these tools are not tailored towards the characteristics of adaptation problems in autonomous systems. In this paper, we present Optimal by Design (ObD ), a framework for model-based requirements-driven synthesis of optimal adaptation strategies for autonomous systems. ObD proposes a model (and a language) for the high-level description of the basic elements of self-adaptive systems, namely the system, capabilities, requirements and environment. Based on those elements, a Markov Decision Process (MDP) is constructed to compute the optimal strategy or the most rewarding system behaviour. Furthermore, this defines a reflex controller that can ensure timely responses to changes. One novel feature of the framework is that it benefits both from goal-oriented techniques, developed for requirement elicitation, refinement and analysis, and synthesis capabilities and extensive research around MDPs, their extensions and tools. Our preliminary evaluation results demonstrate the practicality and advantages of the framework.