Methods and metrics for selective regression testing

In corrective software maintenance, selective regression testing includes test selection from previously-run test suites and test coverage identification. We propose three reduction-based regression test selection methods and two McCabe-based coverage identification metrics (T. McCabe, 1976). We empirically compare these methods with three other reduction- and precision-oriented methods, using 60 test problems. The comparison shows that our proposed methods yield favourable result

ArchOptions: A Real Options-Based Model for Predicting the Stability of Software Architectures

Architectural stability refers to the extent an architecture is flexible to endure evolutionary changes in stakeholders\' requirements and the environment. We assume that the primary goal of software architecture is to guide the system\'s evolution. We contribute to a novel model that exploits options theory to predict architectural stability. The model is predictive: it provides \"insights\" on the evolution of the software system based on valuing the extent an architecture can endure a set of likely evolutionary changes. The model builds on Black and Scholes financial options theory (Noble Prize wining) to value such extent. We show how we have derived the model: the analogy and assumptions made to reach the model, its formulation, and possible interpretations. We refer to this model as ArchOptions

Applying ArchOptions to value the payoff of refactoring

ArchOptions is a real-options based model that we have pro-posed to value the flexibility of software architectures in response to future changes in requirements. In this paper, we build on ArchOptions to devise an options-based model, which values the architectural flexibility that results from a refactoring exercise. This value assists in understanding the payoff of investing in refactoring: if the refactored system results in an architecture that is more flexible, such that the expected added value (in the form of options) due to the en-hanced flexibility outweighs the cost of investing in this exer-cise, then refactoring is said to payoff. We apply our model to a refactoring case study from the literature

Evaluating Software Architectures: Development Stability and Evolution

We survey seminal work on software architecture evaluationmethods. We then look at an emerging class of methodsthat explicates evaluating software architectures forstability and evolution. We define architectural stabilityand formulate the problem of evaluating software architecturesfor stability and evolution. We draw the attention onthe use of Architectures Description Languages (ADLs) forsupporting the evaluation of software architectures in generaland for architectural stability in specific

Using real options to select stable Middleware-induced software architectures

The requirements that force decisions towards building distributed system architectures are usually of a non-functional nature. Scalability, openness, heterogeneity, and fault-tolerance are examples of such non-functional requirements. The current trend is to build distributed systems with middleware, which provide the application developer with primitives for managing the complexity of distribution, system resources, and for realising many of the non-functional requirements. As non-functional requirements evolve, the `coupling' between the middleware and architecture becomes the focal point for understanding the stability of the distributed software system architecture in the face of change. It is hypothesised that the choice of a stable distributed software architecture depends on the choice of the underlying middleware and its flexibility in responding to future changes in non-functional requirements. Drawing on a case study that adequately represents a medium-size component-based distributed architecture, it is reported how a likely future change in scalability could impact the architectural structure of two versions, each induced with a distinct middleware: one with CORBA and the other with J2EE. An option-based model is derived to value the flexibility of the induced-architectures and to guide the selection. The hypothesis is verified to be true for the given change. The paper concludes with some observations that could stimulate future research in the area of relating requirements to software architectures

Self-adaptive and sensitivity-aware QoS modeling for the cloud

Given the elasticity, dynamicity and on-demand nature of the cloud, cloud-based applications require dynamic models for Quality of Service (QoS), especially when the sensitivity of QoS tends to fluctuate at runtime. These models can be autonomically used by the cloud-based application to correctly self-adapt its QoS provision. We present a novel dynamic and self-adaptive sensitivity-aware QoS modeling approach, which is fine-grained and grounded on sound machine learning techniques. In particular, we combine symmetric uncertainty with two training techniques: Auto-Regressive Moving Average with eXogenous inputs model (ARMAX) and Artificial Neural Network (ANN) to reach two formulations of the model. We describe a middleware for implementing the approach. We experimentally evaluate the effectiveness of our models using the RUBiS benchmark and the FIFA 1998 workload trends. The results show that our modeling approach is effective and the resulting models produce better accuracy when compared with conventional models

Symbiotic and sensitivity-aware architecture for globally-optimal benefit in self-adaptive cloud

Due to the uncertain and dynamic demand for Quality of Service (QoS) in cloud-based systems, engineering self-adaptivity in cloud architectures require novel approaches to support on-demand elasticity. The architecture should dynamically select an elastic strategy, which optimizes the global benefit for QoS and cost objectives for all cloud-based services. The architecture shall also provide mechanisms for reaching the strategy with minimal overhead. However, the challenge in the cloud is that the nature of objectives (e.g., throughput and the required cost) and QoS interference could cause overlapping sensitivity amongst intra-and inter-services objectives, which leads to objective-dependency (i.e., conflicted or harmonic) during optimization. In this paper, we propose a symbiotic and sensitivity-aware architecture for optimizing global-benefit with reduced overhead in the cloud. The architecture dynamically partitions QoS and cost objectives into sensitivity independent regions, where the local optimums are achieved. In addition, the architecture realizes the concept of symbiotic feedback loop, which is a bio-directional self-adaptive action that not only allows to dynamically monitor and adapt the managed services by scaling to their demand, but also to adaptively consolidate the managing system by re-partitioning the regions based on symptoms. We implement the architecture as a prototype extending on decentralized MAPE loop by introducing an Adaptor component. We then experimentally analyze and evaluate our architecture using hypothetical scenarios. The results reveal that our symbiotic and sensitivity-aware architecture is able to produce even better global benefit and smaller overhead in contrast to other non sensitivity-aware architectures