Program transformations using temporal logic side conditions

This paper describes an approach to program optimisation based on transformations, where temporal logic is used to specify side conditions, and strategies are created which expand the repertoire of transformations and provide a suitable level of abstraction. We demonstrate the power of this approach by developing a set of optimisations using our transformation language and showing how the transformations can be converted into a form which makes it easier to apply them, while maintaining trust in the resulting optimising steps. The approach is illustrated through a transformational case study where we apply several optimisations to a small program

Component-wise incremental LTL model checking

Efficient symbolic and explicit-state model checking approaches have been developed for the verification of linear time temporal logic (LTL) properties. Several attempts have been made to combine the advantages of the various algorithms. Model checking LTL properties usually poses two challenges: one must compute the synchronous product of the state space and the automaton model of the desired property, then look for counterexamples that is reduced to finding strongly connected components (SCCs) in the state space of the product. In case of concurrent systems, where the phenomenon of state space explosion often prevents the successful verification, the so-called saturation algorithm has proved its efficiency in state space exploration. This paper proposes a new approach that leverages the saturation algorithm both as an iteration strategy constructing the product directly, as well as in a new fixed-point computation algorithm to find strongly connected components on-the-fly by incrementally processing the components of the model. Complementing the search for SCCs, explicit techniques and component-wise abstractions are used to prove the absence of counterexamples. The resulting on-the-fly, incremental LTL model checking algorithm proved to scale well with the size of models, as the evaluation on models of the Model Checking Contest suggests

Managing Schema Change in an Heterogeneous Environment

Change is inevitable even for persistent information. Effectively managing change of persistent information, which includes the specification, execution and the maintenance of any derived information, is critical and must be addressed by all database systems. Today, for every data model there exists a well-defined set of change primitives that can alter both the structure (the schema) and the data. Several proposals also exist for incrementally propagating a primitive change to any derived information (or view). However, existing support is lacking in two ways. First, change primitives as presented in literature are very limiting in terms of their capabilities allowing users to simply add or remove schema elements. More complex types of changes such the merging or splitting of schema elements are not supported in a principled manner. Second, algorithms for maintaining derived information often do not account for the potential heterogeneity between the source and the target. The goal of this dissertation is to provide solutions that address these two key issues. The first part of this dissertation addresses the challenge of expressing a rich complex set of changes. We propose the SERF (Schema Evolution through an Extensible, Re-usable and Flexible) framework that allows users to perform a wide range of complex user-defined schema transformations. Our approach combines existing schema evolution primitives using OQL (object query language) as the glue logic. Within the context of this work, we look at the different domains in which SERF can be applied, including web site management. To further enrich our framework, we also investigate the optimization and verification of SERF transformations. The second part of this dissertation addresses the problem of maintaining views in the face of source changes when the source and the view are not in the same data model. With today\u27s increasing heterogeneity in information structure, it is critical that maintenance of views addresses the data model boundaries. However, view definitions that go across data models are limited to hard-coded algorithms, thereby making it difficult to develop general maintenance algorithms. We provide a two-step solution for this problem. We have developed a cross algebra, that defines views such that there is no restriction that forces the view and the source data models to be the same. We then define update propagation algorithms that can propagate changes from source to target irrespective of the exact translation and the data models. We validate our ideas by applying them to translation and change propagation between the XML and relational data models