Biblio

Today’s world is witnessing a shift from human-written software to machine-learned software, with the rise of systems that rely on machine learning. These systems typically operate in non-static environments, which are prone to unexpected changes, as is the case of self-driving cars and enterprise systems. In this context, machine-learned software can misbehave. Thus, it is paramount that these systems are capable of detecting problems with their machined-learned components and adapt themselves to maintain desired qualities. For instance, a fraud detection system that cannot adapt its machine-learned model to efficiently cope with emerging fraud patterns or changes in the volume of transactions is subject to losses of millions of dollars. In this paper, we take a first step towards the development of a framework aimed to self-adapt systems that rely on machine-learned components. We describe: (i) a set of causes of machine-learned component misbehavior and a set of adaptation tactics inspired by the literature on machine learning, motivating them with the aid of a running example; (ii) the required changes to the MAPE-K loop, a popular control loop for self-adaptive systems; and (iii) the challenges associated with developing this framework. We conclude the paper with a set of research questions to guide future work.

Self-adaptive systems overcome many of the limitations of human supervision in complex software-intensive systems by endowing them with the ability to automatically adapt their structure and behavior in the presence of runtime changes. However, adaptation in some classes of systems (e.g., safetycritical) can benefit by receiving information from humans (e.g., acting as sophisticated sensors, decision-makers), or by involving them as system-level effectors to execute adaptations (e.g., when automation is not possible, or as a fallback mechanism). However, human participants are influenced by factors external to the system (e.g., training level, fatigue) that affect the likelihood of success when they perform a task, its duration, or even if they are willing to perform it in the first place. Without careful consideration of these factors, it is unclear how to decide when to involve humans in adaptation, and in which way. In this paper, we investigate how the explicit modeling of human participants can provide a better insight into the trade-offs of involving humans in adaptation. We contribute a formal framework to reason about human involvement in self-adaptation, focusing on the role of human participants as actors (i.e., effectors) during the execution stage of adaptation. The approach consists of: (i) a language to express adaptation models that capture factors affecting human behavior and its interactions with the system, and (ii) a formalization of these adaptation models as stochastic multiplayer games (SMGs) that can be used to analyze humansystem-environment interactions. We illustrate our approach in an adaptive industrial middleware used to monitor and manage sensor networks in renewable energy production plants.

Security features are often hardwired into software applications, making it difficult to adapt security responses to reflect changes in runtime context and new attacks. In prior work, we proposed the idea of architecture-based self-protection as a way of separating adaptation logic from application logic and providing a global perspective for reasoning about security adaptations in the context of other business goals. In this paper, we present an approach, based on this idea, for combating denial-of-service (DoS) attacks. Our approach allows DoS-related tactics to be composed into more sophisticated mitigation strategies that encapsulate possible responses to a security problem. Then, utility-based reasoning can be used to consider different business contexts and qualities. We describe how this approach forms the underpinnings of a scientific approach to self-protection, allowing us to reason about how to make the best choice of mitigation at runtime. Moreover, we also show how formal analysis can be used to determine whether the mitigations cover the range of conditions the system is likely to encounter, and the effect of mitigations on other quality attributes of the system. We evaluate the approach using the Rainbow self-adaptive framework and show how Rainbow chooses DoS mitigation tactics that are sensitive to different business contexts.