Incremental validation of policy-based systems

Policy-based systems are gaining popularity as a way to manage applications with dynamic behaviour. These systems have policies specifying the desired behaviour, entered into the system by either end-users or system administrators. In order to assure that the policies don't violate any stipulated properties of the system or conflict with one another, the policies must be validated. This validation process can take a very large amount of time as the system's policy base grows. This thesis suggests an incremental validation method, whereby a system which has been determined to be consistent can be validated when a new rule is added to the system. "Trigger chaining" is a concept introduced in this thesis that examines which policies are triggered by the firing of a particular policy. This concept leads to new kinds of conflicts. An algorithm is suggested for incremental detection of such conflicts and is shown to operate in linear time, as opposed to complete revalidation which has quadratic complexity. Trigger chaining also leads to the detection of cyclic conflicts which are briefly discussed. Decision tables are suggested as a suitable format for the internal representation of policies. This format provides a method of checking a policy set for completeness and could help in checking for conflicts. Also decision tables are shown to be a natural format for storing policies. It is also known how to convert decision tables into executable rules, making the transition from decision table-based policies to rule engine policies a simple on

Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks

Deep neural networks (DNNs) have become a widely deployed model for numerous machine learning applications. However, their fixed architecture, substantial training cost, and significant model redundancy make it difficult to efficiently update them to accommodate previously unseen data. To solve these problems, we propose an incremental learning framework based on a grow-and-prune neural network synthesis paradigm. When new data arrive, the neural network first grows new connections based on the gradients to increase the network capacity to accommodate new data. Then, the framework iteratively prunes away connections based on the magnitude of weights to enhance network compactness, and hence recover efficiency. Finally, the model rests at a lightweight DNN that is both ready for inference and suitable for future grow-and-prune updates. The proposed framework improves accuracy, shrinks network size, and significantly reduces the additional training cost for incoming data compared to conventional approaches, such as training from scratch and network fine-tuning. For the LeNet-300-100 and LeNet-5 neural network architectures derived for the MNIST dataset, the framework reduces training cost by up to 64% (63%) and 67% (63%) compared to training from scratch (network fine-tuning), respectively. For the ResNet-18 architecture derived for the ImageNet dataset and DeepSpeech2 for the AN4 dataset, the corresponding training cost reductions against training from scratch (network fine-tunning) are 64% (60%) and 67% (62%), respectively. Our derived models contain fewer network parameters but achieve higher accuracy relative to conventional baselines

Learning an Approximate Model Predictive Controller with Guarantees

A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g. neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding's Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.Comment: 6 pages, 3 figures, to appear in IEEE Control Systems Letter

Testing in the incremental design and development of complex products

Testing is an important aspect of design and development which consumes significant time and resource in many companies. However, it has received less research attention than many other activities in product development, and especially, very few publications report empirical studies of engineering testing. Such studies are needed to establish the importance of testing and inform the development of pragmatic support methods. This paper combines insights from literature study with findings from three empirical studies of testing. The case studies concern incrementally developed complex products in the automotive domain. A description of testing practice as observed in these studies is provided, confirming that testing activities are used for multiple purposes depending on the context, and are intertwined with design from start to finish of the development process, not done after it as many models depict. Descriptive process models are developed to indicate some of the key insights, and opportunities for further research are suggested

Principles in Patterns (PiP) : Institutional Approaches to Curriculum Design Institutional Story

The principal outputs of the PiP Project surround the Course and Class Approval (C-CAP) system. This web-based system built on Microsoft SharePoint addresses and resolves many of the issues identified by the project. Generally well received by both academic and support staff, the system provides personalised views, adaptive forms and contextualised support for all phases of the approval process. Although the system deliberately encapsulates and facilitates existing approval processes thus achieving buy-in, it is already achieving significant improvements over the previous processes, not only in reducing the administrative overheads but also in supporting curriculum design and academic quality. The system is now embedded across three faculties and is now considered by the University of Strathclyde to be a "core institutional service". Alongside the C-CAP system the PiP Project also cultivated a suite of approaches: an incremental systems development methodology; a structured and replicable evaluation approach, and; Strathclyde's Lean Approach to Efficiencies in Education Kit (SLEEK) business process improvement methodology Each is based on recognised formal techniques, providing the basis for a rigorous approach. This is contextualised within and adapted to the HE institutional context thus building the foundation not only for the project but ultimately for institution wide process improvement. This "institutional story" report summarises the principal outcomes of the Project

Abstract Interpretation-based verification/certification in the ciaoPP system

CiaoPP is the abstract interpretation-based preprocessor of the Ciao multi-paradigm (Constraint) Logic Programming system. It uses modular, incremental abstract interpretation as a fundamental tool to obtain information about programs. In CiaoPP, the semantic approximations thus produced have been applied to perform high- and low-level optimizations during program compilation, including transformations such as múltiple abstract specialization, parallelization, partial evaluation, resource usage control, and program verification. More recently, novel and promising applications of such semantic approximations are being applied in the more general context of program development such as program verification. In this work, we describe our extensión of the system to incorpórate Abstraction-Carrying Code (ACC), a novel approach to mobile code safety. ACC follows the standard strategy of associating safety certificates to programs, originally proposed in Proof Carrying- Code. A distinguishing feature of ACC is that we use an abstraction (or abstract model) of the program computed by standard static analyzers as a certifícate. The validity of the abstraction on the consumer side is checked in a single-pass by a very efficient and specialized abstractinterpreter. We have implemented and benchmarked ACC within CiaoPP. The experimental results show that the checking phase is indeed faster than the proof generation phase, and that the sizes of certificates are reasonable. Moreover, the preprocessor is based on compile-time (and run-time) tools for the certification of CLP programs with resource consumption assurances