41698948
An Integrated Approach to Test Tracking and Analysis
This paper presents an integrated approach to test tracking and analysis in a commercial software development environment. Our approach includes measurement gathering, graphical test activity and defect tracking, overall quality assessment using various reliability growth models, and identification of problematic areas and risk management using tree-based modeling. Various existing software tools have been adapted and integrated to support our analysis and modeling. This approach has been used in the testing of several large commercial software products and demonstrated to be effective and efficient under our environment.
5796465
A hierarchical strategy for testing Web-based applications and ensuring their reliability
After examining the specific problems of testing and quality assurance for Web-based applications, we propose a strategy by integrating existing testing techniques and reliability analyses in a hierarchical framework. This strategy combines various usage models for statistical testing to perform high level testing and to guide selective testing of critical and frequently used subparts or components using traditional coverage-based structural testing. Reliability analysis and risk identification form an integral part of this strategy to help assure and improve the overall reliability for Web-based applications. Some preliminary results are included to demonstrate the general viability and effectiveness of our approach.
16987100
Measuring Prime Program Complexity
Abstract This paper uses the prime program decomposition of a program as the basis for a measure that closely correlates with our intuitive notion of program complexity. This measure is based upon the information theory ideas of randomness and entropy such that results about structured programming, data abstractions, and other programming paradigms can be stated in quantitative terms, and empirical means can be used to validate the assumptions used to develop the model. As a graph-based model, it can be applied to several graphical examples as extensions not otherwise available to source-code based models. This paper introduces the measure, derives several properties for it, and gives some simple examples to demonstrate that the measure is a plausible approximation of our notions concerning structured programming.
10468208
Measurement and defect modeling for a legacy software system
This paper analyzes the quality of a large-scale legacy software system using selected metrics. Quality measurements include defect information collected during product development and in-field operation. Other software metrics include measurements on various product and process attributes, including design, size, change, and complexity. Preliminary analyses revealed the high degree of skew in our data and a weak correlation between defects and software metrics. Tree-based models were then used to uncover relationships between defects and software metrics, and to identify high-defect modules together with their associated measurement characteristics. As results presented in tree forms are natural to the decision process and are easy to understand, tree-based modeling is shown to be suitable for change solicitation and useful in guiding remedial actions for quality improvement.
33942395
Measuring and Modeling Usage and Reliability for Statistical Web Testing
Statistical testing and reliability analysis can be used effectively to assure quality for Web applications. To support this strategy, we extract Web usage and failure information from existing Web logs. The usage information is used to build models for statistical Web testing. The related failure information is used to measure the reliability of Web applications and the potential effectiveness of statistical Web testing. We applied this approach to analyze some actual Web logs. The results demonstrated the viability and effectiveness of our approach.
8007887
An application of decision theory for the evaluation of software prototypes
Abstract There is a need to quantify management decision making in software development. To this end, this article presents an application of concepts from economic decision theory to an aspect of this problem currently under study by many in the software engineering community—how to prototype a design and evaluate the effectiveness of this prototype. The role of prototyping as an experimental tool is analyzed and techniques for defining what to prototype and when it is cost effective are introduced. We demonstrate some of these decision theory techniques to define a model that management can use to select solutions from a set of alternative designs.
10434536
Identifying Linear Causal Effects
This paper concerns the assessment of linear cause-effect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.
16178189
Super-resolution Imaging Using Grid Computing
The super-resolution (SR) imaging is to overcome the inherent limitations of the image acquisition systems to produce high-resolution images from their low-resolution counterparts. In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully developed and shown as a promising stochastic approach for addressing the SR problem. However, the MCMC SR approach requires substantial amounts of computational resources, for it not only needs to generate a huge number of samples, but also requires an exhaustive search for obtaining an optimal prior image model. To tackle the above computation challenge, Grid computing is introduced for tackling the SR problem in this paper. The computationally- intensive MCMC SR task is broke down into a set of independent and small sub-tasks, which are further distributed and implemented in the grid computing environment. Their respective results are finally assembled to produce a high-resolution image as the final result of the entire MCMC SR task. Experiments are conducted to show that grid computing can effectively accelerating the execution time of the MCMC SR algorithm.
14734990
Reversible data-embedding with a hierarchical structure
Reversible data-embedding embeds data into a digital content (such as digital image, audio or video) such that an authorized party could decode the embedded data and completely restore the original content. In this paper, we present a hierarchically structured, multi-layer algorithm for digital images. Its basic component is an invertible sharpen filter called the difference expansion. The embedding capacity of this algorithm is substantially higher than results reported in previous reversible data-embedding algorithms. The visual quality of images after data-embedding is also among the best in the literature.
622959
Markov Chain Monte Carlo Super-Resolution Image Reconstruction with Simultaneous Adaptation of the Prior Image Model
In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully exploited and shown as an effective approach to perform super-resolution image reconstruction. However, one major challenge lies at the selection of the hyperparameter of the prior image model, which affects the degree of regularity imposed by the prior image model, and consequently, the quality of the estimated high-resolution image. To tackle this challenge, in this paper, we propose a novel approach to automatically adapt the model’s hyperparameter during the MCMC process, rather than the exhaustive, off-line search. Experimental results presented show that the proposed hyperparameter adaptation method yields extremely close performance to that of the optimal prior image model case.
15872261
Identifying Direct Causal Effects in Linear Models
This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. Using techniques developed for graphical causal models, we show that a model can be decomposed into a set of submodels such that the identification problem can be solved independently in each submodel. We provide a new identification method that identifies causal effects by solving a set of algebraic equations.