In this paper we propose a searching and recommendation method of TV program contents in order to reduce the information overload problem. Most of previous recommendation approaches are dependent on simple ratings of users to determine preference similarity among the users. But our approach is closely related to the combination of ontology-based TV program searching and content-based filtering based on TV ontology and usage history. We search TV programs by computing the similarity between contents ontologies, filter the candidates with preferences of users, and return the ranked list of TV programs. A subjective experiments show that our proposed method is effective in semantic-based searching and recommendation.

Until now, many ontology matching approaches that are applicable to topic maps have been proposed. However, they assume that two ontologies are given before matching and they do not consider searching and selecting the most appropriate ontology from ontology libraries. In our system, topic maps can be searched by a topic map, which is entered by user in order to be evolved by matching and merging with the most appropriate topic map from available topic maps collection. To provide the best search result to user, we develop a ranking method based on syntactic and semantic structure of topics.

Regression testing is an expensive process used to revalidate modified software. Regression test selection (RTS) techniques reduce the cost of regression testing by selecting a subset of a test suite. Many RTS techniques have been proposed, and studies have shown that they produce savings; other studies have shown that their cost-effectiveness varies with characteristics of the workloads to which they are applied. It seems plausible, however, that another factor that impacts RTS techniques involves the process by which they are applied. In particular, issues such as the frequency with which regression testing is performed affect the techniques. Thus, in earlier work an experiment was conducted to assess the effects of test application frequency on the cost-effectiveness of RTS techniques. The results exposed tradeoffs to consider when using these techniques over a series of releases. This work, however, was limited in external validity; in particular, the programs studied were relatively small. Thus, the previous experiment has been replicated on a large, multi-version program. This second experiment confirms the findings of the first. In particular, the cost of using safe RTS techniques was strongly and negatively affected by testing interval. Conversely, the effectiveness of minimization RTS techniques was strongly and positively affected. Copyright © 2005 John Wiley & Sons, Ltd.

Regression testing is an expensive and frequently executed maintenance process used to revalidate modified software. To improve it, regression test selection (RTS) techniques strive to lower costs without overly reducing effectiveness by carefully selecting a subset of the test suite. Under certain conditions, some can even guarantee that the selected test cases perform no worse than the original test suite.But this ignores certain software development realities such as resource and time constraints that may prevent using RTS techniques as intended (e.g., regression testing must be done overnight, but RTS selection returns two days worth of tests). In practice, testers work around this by prioritizing the test cases and running only those that fit within existing constraints. Unfortunately this generally violates key RTS assumptions, voiding RTS technique guarantees and making regression testing performance unpredictable.Despite this, existing prioritization techniques are memoryless, implicitly assuming that local choices can ensure adequate long run performance. Instead, we proposed a new technique that bases prioritization on historical execution data. We conducted an experiment to assess its effects on the long run performance of resource constrained regression testing. Our results expose essential tradeoffs that should be considered when using these techniques over a series of software releases.

Regression testing is the process of validating modified software to detect whether new errors have been introduced into previously tested code and to provide confidence that modifications are correct. Since regression testing is an expensive process, researchers have proposed regression test selection techniques as a way to reduce some of this expense. These techniques attempt to reduce costs by selecting and running only a subset of the test cases in a program's existing test suite. Although there have been some analytical and empirical evaluations of individual techniques, to our knowledge only one comparative study, focusing on one aspect of two of these techniques, has been reported in the literature. We conducted an experiment to examine the relative costs and benefits of several regression test selection techniques. The experiment examined five techniques for reusing test cases, focusing on their relative ablilities to reduce regression testing effort and uncover faults in modified programs. Our results highlight several differences between the techiques, and expose essential trade-offs that should be considered when choosing a technique for practical application.

Regression testing is an expensive maintenance process used to revalidate modified software. Regression test selection (RTS) techniques try to lower the cost of regression testing by selecting and running a subset of the existing test cases. Many such techniques have been proposed and initial studies show that they can produce savings. We believe, however, that issues such as the frequency with which testing is done have a strong effect on the behavior of these techniques. Therefore, we conducted an experiment to assess the effects of test application frequency on the costs and benefits of regression test selection techniques. Our results expose essential tradeoffs that should be considered when using these techniques over a series of software releases.

Regression testing is an expensive maintenance process directed at validating modified software. Regression test selection techniques attempt to reduce the cost of regression testing by selecting tests from a program's existing test suite. Many regression test selection techniques have been proposed. Although there have been some analytical and empirical evaluations of individual techniques, to our knowledge only one comparative study, focusing on one aspect of two of these techniques, has been performed. We conducted an experiment to examine the relative costs and benefits of several regression test selection techniques. The experiment examined five techniques for reusing tests, focusing on their relative abilities to reduce regression testing effort and uncover faults in modified programs. Our results highlight several differences between the techniques, and expose essential tradeoffs that should be considered when choosing a technique for practical application.