Code-coverage guided prioritized test generation
Most automatic test generation research focuses on generation of test data from pre-selected program paths or input domains or program specifications. This paper presents a methodology for a full solution to code-coverage-based test case generation, which includes code coverage-based path selection, test data generation and actual test case representation in program's original languages. We implemented this method in an automatic testing framework, eXVantage. Experimental results and industrial trials show that the framework is able to generate tests to achieve program line coverage from 20% to 98% with reduced overall testing effort. Our major contributions include an innovative coverage-based program prioritization algorithm, a novel path selection algorithm that takes into consideration program priority and functional calling relationship, and a constraint solver for test data generation that derives constraints from bytecode and solves complex constraints involving strings and dynamic objects.

title: Code-coverage guided prioritized test generation
abstract: With Internet applications spreading like wildfire, software testing is challenged with new topics related to the distributed nature of Web applications. We apply code based testing techniques to the testing of Web applications, specifically Java programs. Source code based automatic test generation is difficult because most previous methods use constraint satisfaction models as a solution, which is an NP complete problem [M. J. Gallagher et al. (1997)]. We present a method of guiding users through test case generations. Instead of automating the entire procedure, our method aims at generating a framework of test cases and providing instructions for users to instantiate the framework into actual executable test cases. An early experimental study of this method shows the effectiveness of this method.

title: Direct interesting rule generation
abstract: An association rule generation algorithm usually generates too many rules including a lot of uninteresting ones. Many interestingness criteria are proposed to prune those uninteresting rules. However, they work in post-pruning process and hence do not improve the rule generation efficiency. We discuss properties of informative rule set and conclude that the informative rule set includes all interesting rules measured by many commonly used interestingness criteria, and that rules excluded by the informative rule set are forwardly prunable, i.e. they can be removed in the rule generation process instead of post pruning. Based on these properties, we propose a direct interesting rule generation algorithm, DIG, to directly generate interesting rules defined by any of 12 interestingness criteria. We further show experimentally that DIG is faster and uses less memory than Apriori.

title: RACCOON: a peer-based system for data integration and sharing
abstract: Recent database applications see the emerging need to support data integration in distributed, peer-to-peer environments, in which autonomous peers (sources) connected by a network are willing to exchange data and services with each other. To address related research challenges, we are developing a system called "RACCOON", which allows different sources to integrate and share their data. We use an application to show several important features of the RACCOON system. The system also suggests semantic mappings for the user to choose. We show the two different querying modes, particularly how a query is expanded using the semantic mappings in the extended querying mode to compute as many answers to the query as possible.

title: MCA: an end-to-end multicast congestion avoidance scheme with feedback suppression
abstract: In this paper, we propose MCA, an end-to-end multicast congestion avoidance scheme with feedback suppression. Congestion avoidance [ (1989) 1] is different from congestion control in the sense that our scheme detects and responds to network congestion without necessarily inducing packet loss. Our scheme is a single-rate scheme and operates end-to-end, i.e. the sending rate is controlled by the source based on feedback from the most congested receiver and does not expect packet marking or other support from intermediate nodes. We design it to be robust under both lossless and lossy situations. Congestion is detected at receivers using the concept of accumulation (the number of buffered bits of a flow inside the network) and simple thresholding techniques proposed in our recent unicast work [Accumulation-based congestion control (submitted)]. For the purposes of choosing representative (the most congested receiver) by the source and suppressing feedback by receivers, each receiver maintains its Good Throughput Rate At Congestion (G-TRAC) (the product of receiving rate during congestion epochs and 1-f, where f is congestion occurrence frequency).In this way, receivers do not need to continuously (either densely or sparsely) exchange packets with the sender (e.g. to measure RTT). Therefore, MCA is scalable for large-size groups. We evaluate the design and demonstrate the performance of MCA using detailed ns-2 simulations.

title: Parallel computing of a quasi-Monte Carlo algorithm for valuing derivatives
abstract: The performance of the standard Monte Carlo method is compared with the performance obtained through the use of ( t , m , s )-nets in base b in the approximation of several high dimensional integral problems in valuing derivatives and other securities. The ( t , m , s )-nets are generated by a parallel algorithm, where particular considerations are given to scalability of dynamic adaptive routing and load balancing in the design and implementation of the algorithm. From the numerical evidence it appears that such nets can be powerful tools for valuing such securities.

title: Introducing a Rule Importance Measure
abstract: Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based Rule Importance Measure to select the most important rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule generated across all the rule sets. Rule importance is different from either rule interestingness measures or rule quality measures because of their application tasks, the processes where the measures are applied and the contents they measure. The experimental results from an artificial data set, UCI machine learning datasets and an actual geriatric care medical data set show that our method reduces the computational cost for rule generation and provides an effective measure of how important is a rule.