Kristopher Welsh

Towards requirements aware systems: Run-time resolution of design-time assumptions

In earlier work we proposed the idea of requirements-aware systems that could introspect about the extent to which their goals were being satisfied at runtime. When combined with requirements monitoring and self adaptive capabilities, requirements awareness should help optimize goal satisfaction even in the presence of changing run-time context. In this paper we describe initial progress towards the realization of requirements-aware systems with REAssuRE. REAssuRE focuses on explicit representation of assumptions made at design time. When such assumptions are shown not to hold, REAssuRE can trigger system adaptations to alternative goal realization strategies.

Understanding the Scope of Uncertainty in Dynamically Adaptive Systems

[Context and motivation] Dynamically adaptive systems are increasingly conceived as a means to allow operation in changeable or poorly understood environments. [Question/problem] This can result in the selection of solution strategies based on assumptions that may not be well founded. [Principle ideas/results] This paper proposes the use of claims in goal models as a means to reason about likely sources of uncertainty in dynamically adaptive systems. Accepting that such claims can’t be easily validated at design-time, we should instead evaluate how the system will behave if a claim is proven false by developing a validation scenario. [Contribution] Validation scenarios may be costly to evaluate so the approach we advocate is designed to carefully select only those claims that are less certain, or whose falsification would have serious consequences.

Self-Explanation in Adaptive Systems

The behaviour of self adaptive systems can be emergent. The difficulty in predicting the systems behaviour means that there is scope for the system to surprise its customers and its developers. Because its behaviour is emergent, a self-adaptive system needs to garner confidence in its customers and it needs to resolve any surprise on the part of the developer during testing and mainteinance. We believe that these two functions can only be achieved if a self-adaptive system is also capable of self-explanation. We argue a self-adaptive systems behaviour needs to be explained in terms of satisfaction of its requirements. Since self-adaptive system requirements may themselves be emergent, a means needs to be found to explain the current behaviour of the system and the reasons that brought that behaviour about. We propose the use of goal-based models during runtime to offer self-explanation of how a system is meeting its requirements, and why the means of meeting these were chosen. We discuss the results of early experiments in self-explanation, and set out future work.

When to Adapt? Identification of Problem Domains for Adaptive Systems

Dynamically adaptive systems (DASs) change behaviour at run-time to operate in volatile environments. As we learn how best to design and build systems with greater autonomy, we must also consider when to do so. Thus far, DASs have tended to showcase the benefits of adaptation infrastructures with little understanding of what characterizes the problem domains that require run-time adaptation. This position paper posits that context-dependent variation in the acceptable trade-offs between non-functional requirements is a key indicator of problems that require dynamically adaptive solutions.

Self-Explanation in Adaptive Systems Based on Runtime Goal-Based Models

The behaviour of self adaptive systems can be emergent, which means that the system’s behaviour may be seen as unexpected by its customers and its developers. Therefore, a self-adaptive system needs to garner confidence in its customers and it also needs to resolve any surprise on the part of the developer during testing and maintenance. We believe that these two functions can only be achieved if a self-adaptive system is also capable of self-explanation. We argue a self-adaptive system’s behaviour needs to be explained in terms of satisfaction of its requirements. Since self-adaptive system requirements may themselves be emergent, we propose the use of goal-based requirements models at runtime to offer self-explanation of how a system is meeting its requirements. We demonstrate the analysis of run-time requirements models to yield a self-explanation codified in a domain specific language, and discuss possible future work.

Run-time resolution of uncertainty

Requirements awareness should help optimize requirements satisfaction when factors that were uncertain at design time are resolved at runtime. We use the notion of claims to model assumptions that cannot be verified with confidence at design time. By monitoring claims at runtime, their veracity can be tested. If falsified, the effect of claim negation can be propagated to the systems goal model and an alternative means of goal realization selected automatically, allowing the dynamic adaptation of the system to the prevailing environmental context.

Managing Testing Complexity in Dynamically Adaptive Systems: A Model-Driven Approach

Autonomous systems are increasingly conceived as a means to allow operation in changeable or poorly understood environments. However, granting a system autonomy over its operation removes the ability of the developer to be completely sure of the systems behaviour under all operating contexts. This combination of environmental and behavioural uncertainty makes the achievement of assurance through testing very problematic. This paper focuses on a class of system, called an m-DAS, that uses run-time models to drive run-time adaptations in changing environmental conditions. We propose a testing approach which is itself model-driven, using model analysis to significantly reduce the set of test cases needed to test for emergent behaviour. Limited testing resources may therefore be prioritised for the most likely scenarios in which emergent behaviour may be observed.

Run-time model evaluation for requirements model-driven self-adaptation

A self-adaptive system adjusts its configuration to tolerate changes in its operating environment. To date, requirements modeling methodologies for self-adaptive systems have necessitated analysis of all potential system configurations, and the circumstances under which each is to be adopted. We argue that, by explicitly capturing and modelling uncertainty in the operating environment, and by verifying and analysing this model at runtime, it is possible for a system to adapt to tolerate some conditions that were not fully considered at design time. We showcase in this paper our tools and research results.

Tracing requirements for adaptive systems using claims

The complexity of environments faced by dynamically adaptive systems (DAS) means that the RE process will often be iterative with analysts revisiting the system specifications based on new environmental understanding product of experiences with experimental deployments, or even after final deployments. An ability to trace backwards to an identified environmental assumption, and to trace forwards to find the areas of a DASs specification that are affected by changes in environmental understanding aids in supporting this necessarily iterative RE process. This paper demonstrates how claims can be used as markers for areas of uncertainty in a DAS specification. The paper demonstrates backward tracing using claims to identify faulty environmental understanding, and forward tracing to allow generation of new behaviour in the form of policy adaptations and models for transitioning the running system.

Foreword: 2nd Workshop requirements@run.time

The 2nd edition of the Workshop requirements@run.time was held at the 19th International Conference on Requirements Engineering (RE 2011) in the city of Trento, Italy on the 30th of August 2011. It was organized by Nelly Bencomo, Emmanuel Letier, Jon Whittle, Anthony Finkelstein, and Kris Welsh. This foreword presents a digest of the discussions and presentations that took place during the workshop.