17865149
A framework for specification-based class testing
Class testing is the base of object-oriented software testing. It involves three aspects: testing each method, testing the relations among class methods and testing the inheriting relation between class and subclass. Rather than concerning the whole class testing process, most specification-based class testing focuses on the methods of generating test cases from the class specification. As a result, the class testing process and test cases cannot be unified and managed in a consistent and convenient way. This paper introduces a test class framework (TCF) that is used to structure the test cases and test deriving process of the class under testing. This framework clearly denotes the process of deriving test cases and test suites from a class specification. It facilitates the construction and management of test cases. Object-Z notation is used to express the framework and class specification.
35610063
A Specification-Based Approach to Testing Polymorphic Attributes
The object-oriented features, such as aggregation, inheritance and polymorphism, facilitate software reuse and improve software quality. But those features also cause new problems that traditional testing techniques cannot resolve effectively. Existing researches on object-oriented software testing are mainly based on some graph representations and seldom consider the combinational effect of aggregation, inheritance and polymorphism. For some model-based specifications that do not include graph representations, it is difficult to use the graph based testing techniques to derive test cases. This paper provides an approach to deriving test cases for testing the combinational effect of aggregation, inheritance and polymorphism from a model-based class specification. This approach contains two techniques at intra-class and inter-class testing levels and is mechanized using the algorithms presented in this paper. Finally, through the experimental analysis, we present the effectiveness and applicability of our testing approach.
11093943
Distributed query sampling: a quality-conscious approach
We present an adaptive distributed query-sampling framework that is quality-conscious for extracting high-quality text database samples. The framework divides the query-based sampling process into an initial seed sampling phase and a quality-aware iterative sampling phase. In the second phase the sampling process is dynamically scheduled based on estimated database size and quality parameters derived during the previous sampling process. The unique characteristic of our adaptive query-based sampling framework is its self-learning and self-configuring ability based on the overall quality of all text databases under consideration. We introduce three quality-conscious sampling schemes for estimating database quality, and our initial results show that the proposed framework supports higher-quality document sampling than existing approaches.
694549
Optimizing Multiple Distributed Stream Queries Using Hierarchical Network Partitions
We consider the problem of query optimization in distributed data stream systems where multiple continuous queries may be executing simultaneously. In order to achieve the best performance, query planning (such as join ordering) must be considered in conjunction with deployment planning (e.g., assigning operators to physical nodes with optimal ordering). However, such a combination involves not only a large number of network nodes but also many query operators, resulting in an extremely large search space for optimal solutions. Our paper aims at addressing this problem by utilizing hierarchical network partitions. We propose two algorithms - top-down and bottom-up which utilize hierarchical network partitions to provide scalable query optimization. Formal analysis is presented to establish the bounds on the search-space and to show the sub-optimality of our algorithms. Through simulations and experiments using a prototype deployed on Emulab we demonstrate the effectiveness of our algorithms.
1693623
Hooking into Object-Oriented Application Frameworks
An object-oriented application framework provides a generic design within a given domain and a reusable implementation of that design. Frameworks have great potential for reuse, but that potential can only be achieved if the framework can be understood and used effectively by application developers. The complexity of the design and implementation of a framework can make understanding the framework difficult, and so good documentation and guidance to the framework user is needed. Several methods exist for documenting the design of the framework, but less work has been done on documenting the purpose of the framework and how it is intended to be used. In this paper, we introduce the concept of hooks as a means of documenting and providing guidance on the intended use of a framework. Each hook provides a concise solution to a focused requirement or problem within the context of the framework. Solutions for more complex requirements can be provided by combining several hooks. A discussion and means of describing hooks is given, with a focus on the method of adaption used to fulfill the requirement and the level of support provided for the solution within the framework.
32876579
Medical image segmentation and retrieval via deformable models
A new method based on deformable shape models for medical image segmentation is described. Experiments for blood cell micrographs have been conducted to verify the accuracy of the shape model-based segmentation and object shape description method. The cell segmentation method does not require user input for initialization. Coherence information between cells is utilized via a globally consistent cost function. The proposed segmentation method can be used in automated analysis for images of stained blood smear and segmentation of other medical structures. A method for shape population-based retrieval is also described. Results of population-based image queries for a database of blood cell micrographs are shown.
40049540
Key Derivation Algorithms for Monotone Access Structures in Cryptographic File Systems
Advances in networking technologies have triggered the “storage as a service” (SAS) model. The SAS model allows content providers to leverage hardware and software solutions provided by the storage service providers (SSPs), without having to develop them on their own, thereby freeing them to concentrate on their core business. The SAS model is faced with at least two important security issues: (i) How to maintain the confidentiality and integrity of files stored at the SSPs? (ii) How to efficiently support flexible access control policies on the file system? The former problem is handled using a cryptographic file system, while the later problem is largely unexplored. In this paper, we propose secure, efficient and scalable key management algorithms to support monotone access structures on large file systems. We use key derivation algorithms to ensure that a user who is authorized to access a file, can efficiently derive the file's encryption key. However, it is computationally infeasible for a user to guess the encryption keys for those files that she is not authorized to access. We present concrete algorithms to efficiently and scaleably support a discretionary access control model (DAC) and handle dynamic access control updates & revocations. We also present a prototype implementation of our proposal on a distributed file system. A trace driven evaluation of our prototype shows that our algorithms meet the security requirements while incurring a low performance overhead on the file system.