Modelling Requirements for Content Recommendation Systems

This paper addresses the modelling of requirements for a content Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user switches roles constantly between content generator and content receiver. The goals and softgoals are different when the user is generating a post, as opposed as replying to a post. In other words, the user is generating instances of different entities, depending on the role she has: a generator generates instances of a "post", while the receiver generates instances of a "reply". Therefore, we believe that when addressing Requirements Engineering (RE) for RS, it is necessary to distinguish these roles clearly. We aim to model an essential dynamic on OSN, namely that when a user creates (posts) content, other users can ignore that content, or themselves start generating new content in reply, or react to the initial posting. This dynamic is key to designing OSNs, because it influences how active users are, and how attractive the OSN is for existing, and to new users. We apply a well-known Goal Oriented RE (GORE) technique, namely i-star, and show that this language fails to capture this dynamic, and thus cannot be used alone to model the problem domain. Hence, in order to represent this dynamic, its relationships to other OSNs' requirements, and to capture all relevant information, we suggest using another modelling language, namely Petri Nets, on top of i-star for the modelling of the problem domain. We use Petri Nets because it is a tool that is used to simulate the dynamic and concurrent activities of a system and can be used by both practitioners and theoreticians.Comment: 28 pages, 7 figure

Self-tuning of software systems through goal-based feedback control loop

Quality requirements of a software system cannot be optimally met, especially when it is running in an uncertain and changing environment. In principle, a controller at runtime can monitor the change impact on quality requirements of the system, update the expectations and priorities from the environment, and take reasonable actions to improve the overall satisfaction. In practice, however, existing controllers are mostly designed for tuning low- level performance indicators rather than high-level requirements. By maintaining a live goal model to represent the runtime requirements and linking the overall satisfaction to an earned business value indicator as feedback, we propose a control-theoretic self-tuning method that can dynamically tune the preferences of different quality requirements, and can autonomously make the tradeoff decisions among different quality requirements through our preference-based goal reasoning. The reasoning result is involved to reconfigure the variation points of the goal model, and accordingly mapped to the system architecture reconfiguration. The effectiveness of our self-tuning method is evaluated by comparing the earned business value with the static and ad-hoc methods and analysing the self-tuning process

Reasoning with qualitative preferences for optimization of component-based system development

A component-based system is a set of entities that work together in well-defined ways to satisfy a given requirement specified by the stakeholders for the system. This requirement can be modeled as a set of combinations of traits, which represent acceptable alternatives for providing the required functionality. A system satisfies its requirement if and only if it provides one of the required sets of traits in its entirety. Beyond the requirement, system stakeholders may also have preferences with respect to optional functionality that could be provided by a system, tradeoffs between non-functional properties, or other system design options. This work focuses on integrating support for both qualitative preference reasoning and formal verification into the component-based system design process in order to choose a set of components for the system that, when composed, will (1) satisfy the stakeholders\u27 requirement for the system and (2) provide a set of traits that is optimal with respect to the given preferences. Our primary research objective is to develop a generic, modular, end-to-end framework for developing component-based systems of any type which are correct according to the system requirement and most preferred with respect to the stakeholders\u27 preferences. Applications of the framework to problems in Web service composition, goal-oriented requirements engineering, and other areas will be discussed, along with future work toward integrating multi-stakeholder preference reasoning and partial satisfaction of traits into the framework

Volare Mobile Context-aware Adaptation for the Cloud

As the explosive growth in the proliferation and use of mobile devices accelerates, more web service providers move their premises on the Cloud under the Software as a Service (SaaS) service model. Mobile environments present new challenges that Service Discovery methods developed for non-mobile environments cannot address. The requirements a mobile client device will have from internet services may change, even at runtime, due to variable context, which may include hardware resources, environmental variables (like network availability) and user preferences. Binding to a discovered service having QoS levels different from the ones imposed by current context and policy requirements may lead to low application performance, excessive consumption of mobile resources such as battery life and service disruption, especially for long lasting foreground applications like media-streaming, navigation etc. This thesis presents the Volare approach for performing parameter adaptation for service requests to Cloud services, in SaaS architecture. For this purpose, we introduce an adaptive mobile middleware solution that performs context-aware QoS parameter adaptation. When service discovery is initiated, the middleware calculates the optimal service requests QoS levels under the current context, policy requirements and goals and adapts the service request accordingly. At runtime, it can trigger dynamic service rediscovery following significant context changes, to ensure optimal binding. The adaptation logic is built through the characteristics of the declarative domain-specific Volare Adaptation Policy Specification Language (APSL). Key characteristics of this approach include two-level policy support (providing both device specific and application specific adaptation), integration of a User Preferences Model and high behavioral (parameter adaptation) variability, by allowing multiple weighted adaptation rules to influence each QoS variable. The Volare approach supports unanticipated quantitative long term performance goals (LTPGs) with finite horizons. A use case and a proof-of-concept implementation have been developed on cloud service discovery through a cloud service provider, as well as an appropriate case study, which demonstrates significant savings in battery consumption, provider data usage and monetary cost, compared to unadapted QoS service bindings, while consistently avoiding service disruptions caused by QoS levels that the device cannot support. In addition, adaptation policies using the Volare approach tend to increase in size, in a mostly linear fashion, instead of the combinatorial increase of more conventional situation-action approaches