Recursive Program Optimization Through Inductive Synthesis Proof Transformation

The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality

An Autonomous Engine for Services Configuration and Deployment.

The runtime management of the infrastructure providing service-based systems is a complex task, up to the point where manual operation struggles to be cost effective. As the functionality is provided by a set of dynamically composed distributed services, in order to achieve a management objective multiple operations have to be applied over the distributed elements of the managed infrastructure. Moreover, the manager must cope with the highly heterogeneous characteristics and management interfaces of the runtime resources. With this in mind, this paper proposes to support the configuration and deployment of services with an automated closed control loop. The automation is enabled by the definition of a generic information model, which captures all the information relevant to the management of the services with the same abstractions, describing the runtime elements, service dependencies, and business objectives. On top of that, a technique based on satisfiability is described which automatically diagnoses the state of the managed environment and obtains the required changes for correcting it (e.g., installation, service binding, update, or configuration). The results from a set of case studies extracted from the banking domain are provided to validate the feasibility of this propos

Composing Aspects at Shared Join Points

Aspect-oriented languages provide means to superimpose aspectual behavior on a given set of join points. It is possible that not just a single, but several units of aspectual behavior need to be superimposed on the same join point. Aspects that specify the superimposition of these units are said to "share" the same join point. Such shared join points may give rise to issues such as\ud determining the exact execution order and the dependencies among the aspects. In this paper, we present a detailed analysis of the problem, and identify a set of requirements upon mechanisms for composing aspects at shared join points. To address the identified issues, we propose a general and declarative model for defining constraints upon the possible compositions of aspects at a shared join point. Finally, by using an extended notion of join points, we show how concrete aspectoriented programming languages, particularly AspectJ and Compose*, can adopt the proposed model

Viewpoints and goals: towards an integrated approach

Dissertação de Mestrado em Engenharia InformáticaRequirements elicitation and analysis have been studied according to several approaches that differ mostly on their "orientation", in this case relying on goals or viewpoints. Goal-Oriented approaches such as KAOS rely on goals to direct their process of eliciting requirements: a goal is an objective the system under consideration should achieve and represents a system property that may reflect either a functional (e.g. a service provided by the system) or a non-functional (e.g. security, performance) requirement; its satisfaction may imply the participation of several agents and the resolution of possible obstacles that may arise. The KAOS approach offers an unambiguous method for requirement decomposition and may provide a set of heuristics to approaches where one does not exist. Viewpoint-Oriented approaches such as PREview focus on gathering information pertaining to the problem from several agents that may have different, often equally valid, and incomplete perspectives on the problem. These partial intakes reflect their different responsibilities, roles, goals, or interpretations of the information sources; hence the combination of the agent and its input on the system is called a viewpoint. PREview benefits from a particularly lightweight approach to requirements encapsulation, but fails to provide a set of heuristics for the process of identifying the system's requirements. Considering the issues identified in each approach, it is verifiable that both approaches are complementary: on the one hand, KAOS offers a set of requirements elicitation heuristics through goal decomposition; on the other hand, PREview is a lightweight approach to viewpoint oriented requirements engineering, tailored especially for integration, however lacks a more systematic mechanism to guide the requirements elicitation process. The objective of this dissertation is therefore to propose a hybrid approach that builds on the PREview approach and brings together the benefits of the KAOS approach. The result is synergetic where, for example, completion is better addressed by providing a set of heuristics for requirement elicitation

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

A survey of program transformation with special reference to unfold/fold style program development

This paper consists of a survey of current, and past, work on *program transformation* for the purpose of optimization. We first discuss some of the general methodological frameworks for program modification, such as *analogy*, *explanation based learning*, *partial evaluation*, *proof theoretic optimization*, and the *unfold/fold* technique. These frameworks are not mutually exclusive, and the latter, unfold/fold, is certainly the most widely used technique, in various guises, for program transformation. Thus we shall often have occasion to: compare the relative merits of systems that employ the technique in some form, *and*; compare the unfold/fold systems with those that employ alternative techniques. We also include (and compare with unfold/fold) a brief survey of recent work concerning the use of *formal methods* for program transformation

ExplainIt! -- A declarative root-cause analysis engine for time series data (extended version)

We present ExplainIt!, a declarative, unsupervised root-cause analysis engine that uses time series monitoring data from large complex systems such as data centres. ExplainIt! empowers operators to succinctly specify a large number of causal hypotheses to search for causes of interesting events. ExplainIt! then ranks these hypotheses, reducing the number of causal dependencies from hundreds of thousands to a handful for human understanding. We show how a declarative language, such as SQL, can be effective in declaratively enumerating hypotheses that probe the structure of an unknown probabilistic graphical causal model of the underlying system. Our thesis is that databases are in a unique position to enable users to rapidly explore the possible causal mechanisms in data collected from diverse sources. We empirically demonstrate how ExplainIt! had helped us resolve over 30 performance issues in a commercial product since late 2014, of which we discuss a few cases in detail.Comment: SIGMOD Industry Track 201