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.
37559798
Measurement and continuous improvement of software reliability throughout software life-cycle
Abstract Software reliability is one of the most important aspects of software quality. However, most of the existing techniques focus on the system testing and other late stages of development life-cycle. Therefore, they are not effective for early problem identification and reliability improvement. This paper describes our recent work in establishing predictive linkage between software reliability and other entities that we can measure and control early in the development cycle, and using such predictive relations to drive continuous reliability improvement.
18396642
Defect handling in medium and large open source projects
Open source projects have resulted in numerous high-quality, widely used products. Understanding the defect-handling strategies such projects employ can help us use the publicly accessible defect data from these projects to provide valuable quality-improvement feedback and to better understand the defect characteristics for a wider variety of software products. We conducted a survey to understand defect handling in selected open source projects and compared the particular approaches taken in different projects. We focused on defect handling instead of the broader quality assurance activities other researchers have previously reported. Our results provided quantitative evidence about the current practice of defect handling in an important subset of open source projects.
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.
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.
29205217
Improving the Reliability of Object-Oriented Software through Object-Level Behavioral Simulation
In this paper we propose an Object-level Behavior Simulation Model (OBSM) that simulates the behavior of each object according to the message pattern exhibited by behavioral design pattern to verify it for its correctness. The model evaluates the existing reliability of the object-oriented software and allows developer to improve it during early phases of the development.
9933284
A MCMC approach for Bayesian super-resolution image reconstruction
In this paper, we consider the super-resolution image reconstruction problem. We propose a Markov chain Monte Carlo (MCMC) approach to find the maximum a posterior probability (MAP) estimation of the unknown high-resolution image. Firstly, Gaussian Markov random field (GMRF) is exploited for modeling the prior probability distribution of the unknown high-resolution image. Then, a MCMC technique (in particular, the Gibbs sampler) is introduced to generate samples from the posterior probability distribution to compute the MAP estimation of the unknown high-resolution image, which is obtained as the mean of the samples. Moreover, we derive a bound on the convergence time of the proposed MCMC approach. Finally, the experimental results are presented to verify the superior performance of the proposed approach and the validity of the proposed bound.
3092732
Inequality Constraints in Causal Models with Hidden Variables
We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. We derive instrumental inequality type of constraints on nonexperimental distributions. The results have applications in testing causal models with observational or experimental data.
16586772
A new state-space approach for super-resolution image sequence reconstruction
Super-resolution imaging is to overcome the inherent limitations of image acquisition to create high-resolution images from their low-resolution counterparts. In this paper, a novel state-space approach is proposed to incorporate the temporal correlations among the low-resolution observations into the framework of the Kalman filtering. The proposed approach exploits both the temporal correlations information among the high-resolution images and the temporal correlations information among the low-resolution images to improve the quality of the reconstructed high-resolution sequence. Experimental results show that the proposed framework is superior to bi-linear interpolation, bi-cubic spline interpolation and the conventional Kalman filter approach, due to the consideration of the temporal correlations among the low-resolution images.
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.
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.