1111153
Towards the UML evaluation using taxonomic patterns on meta-classes
In order to evaluate the design quality of the UML, understanding meta-classes is a key activity as they are the primary weapons by which the UML specifies the application domains. The paper introduces taxonomic patterns for clustering the UML meta-classes based on the observation of their evolution and fitness. The result sets of the patterns assist in finding the evidence of the concerns about the UML design and quality. It not only helps to find out problematic meta-classes, possible design defects of the UML and the inconsistency between the UML meta-models and the application domains; but also provides valuable information for guiding the development and evaluation of the UML. The work can be the basis of further quality analysis of the UML meta-models.
13211852
Requirements guided dynamic software clustering
In this paper, we propose a requirements guided dynamic approach to address software clustering -which aims at providing the logically meaningful and high-level decompositions of large and complex systems. In our approach, the hierarchical structure of functional requirements are constructed by a text document clustering technique named hierarchical agglomerative clustering (HAC) as a high-level skeleton to facilitate the further decomposition of source code through dynamic analysis. We also perform an experimental study based on a GNU system and present the quantitative and qualitative analysis of the experimental results.
19304521
Applying OO Metrics to Assess UML Meta-models
UML has been coming of age through more than seven years development, in which there are not only minor revision like from UML 1.1 to UML 1.2, but also significant improvement like final adoption of UML 2.0 submissions. However there is so far lack of an objective assessment to UML meta-models, which can be used to control and predict the evolution of the UML. In this paper we regard UML meta-models as the equivalent of Object-Oriented (OO) design models. Therefore, we can adapt OO design metrics and criteria as a method to assess UML meta-models. Our method conducts the assessment of stability and design quality to UML meta-models in versions of 1.1, 1.3, 1.4 (with Action Semantics), 1.5, and 2.0. Based on the results we analyze the evolution of the UML versions and provide the applicability suggestions to the method.
17777200
A similarity-aware approach to testing based fault localization
Debugging is a time-consuming task in software development and maintenance. To accelerate this task, several approaches have been proposed to automate fault localization. In particular, testing based fault localization (TBFL), which utilizes the testing information to localize the faults, seem to be very promising. However, the similarity between test cases in the test suite has been ignored in the research on TBFL. In this paper, we investigate this similarity issue and propose a novel approach named similarity-aware fault localization (SAFL), which can calculate the suspicion probability of each statement with little impact by the similarity issue. To address and deal with the similarity between test cases, SAFL applies the theory of fuzzy sets to remove the uneven distribution of the test cases. We also performed an experimental study for two real-world programs at different size levels to evaluate SAFL together with another two approaches to TBFL. Experimental results show that SAFL is more effective than the other two approaches when the test suites contain injected redundancy, and SAFL can achieve a competitive result with normal test suites. SAFL can also be more effective than applying test suite reduction to current approaches to TBFL.
15561253
An experimental comparison of four test suite reduction techniques
As a test suite usually contains redundancy, a subset of the test suite (representative set) may still satisfy all the test objectives. As the redundancy increases the cost of executing the test suite, many test suite reduction techniques have been brought out in spite of the NP-completeness of the general problem of finding the optimal representative set of the test suite. In the literature, some experimental studies of test suite reduction techniques have already been reported, but there are still shortcomings of the studies of these techniques. This paper presents an experimental comparison of the four typical test suite reduction techniques: heuristic H, heuristic GRE, genetic algorithm-based approach and ILP-based approach. The aim of the study is to provide a guideline for choosing the appropriate test suite reduction techniques.
31408844
A component-based software configuration management model and its supporting system
Software configuration management (SCM) is an important key technology in software development. Component-based software development (CBSD) is an emerging paradigm in software development. However, to apply CBSD effectively in real world practice, supporting SCM in CBSD needs to be further investigated. In this paper, the objects that need to be managed in CBSD is analyzed and a component-based SCM model is presented. In this model, components, as the integral logical constituents in a system, are managed as the basic configuration items in SCM, and the relationships between/among components are defined and maintained. Based on this model, a configuration management system is implemented.
24410332
VizCluster and its Application on Classifying Gene Expression Data
Visualization enables us to find structures, features, patterns, and relationships in a dataset by presenting the data in various graphical forms with possible interactions. A visualization can provide a qualitative overview of large and complex datasets, can summarize data, and can assist in identifying regions of interest and appropriate parameters focused on quantitative analysis. Recently, DNA microarray technology provides a broad snapshot of the state of the cell, by measuring the expression levels of thousands of genes simultaneously. Such information can thus be used to analyze different samples by gene expression profiles. It has already had a significant impact on the field of bioinformatics, requiring innovative techniques to efficiently and effectively extract, analyze, and visualize these fast growing data.In this paper, we present a dynamic interactive visualization environment, VizCluster, and its application on classifyinggene expression data. VizCluster takes advantage of graphical visualization methods to reveal underlining data patterns. It combines the merits of both high dimensional projection scatter-plot and parallel coordinate plot. In its core lies a nonlinear projection which maps the n-dimensional vectors onto two-dimensional points. To preserve the information at different scales and yet reduce the typical problem of parallel coordinate plots being messy caused by overlapping lines, a zip zooming viewing method is proposed. Integrated with other features, VizCluster is developed to give a simple, fast, intuitive, and yet powerful view of the data set. Its primary applications are on the classification of samples and evaluation of gene clusters for microarray datasets. Three gene expression datasets are used to illustrate the approach. We demonstrate that VizCluster approach is promising to be used for analyzing and visualizing microarray data sets and further development is worthwhile.
2094926
Conflict driven learning in a quantified Boolean Satisfiability solver
Within the verification community, there has been a recent increase in interest in Quantified Boolean Formula evaluation (QBF) as many interesting sequential circuit verification problems can be formulated as QBF instances. A closely related research area to QBF is Boolean Satisfiability (SAT). Recent advances in SAT research have resulted in some very efficient SAT solvers. One of the critical techniques employed in these solvers is Conflict Driven Learning. In this paper, we adapt conflict driven learning for application in a QBF setting. We show that conflict driven learning can be regarded as a resolution process on the clauses. We prove that under certain conditions, tautology clauses obtained from resolution in QBF also obey the rules for implication and conflicts of regular (non-tautology) clauses; and therefore they can be treated as regular clauses and used in future search. We have implemented this idea in a new QBF solver called Quaffle and our initial experiments show that conflict driven learning can greatly speed up the solution process for most of the benchmarks we tested.
206501510
Head pose estimation using Fisher Manifold learning
Here, we propose a new learning strategy for head pose estimation. Our approach uses nonlinear interpolation to estimate the head pose using the learning result from face images of two head poses. Advantage of our method to regression method is that it only requires training images of two head poses and better generalization ability. It outperforms existed methods, such as regression and multiclass classification method, on both synthesis and real face images. Average head pose estimation error of yaw rotation is about 4/sup 0/, which proves that our method is effective in head pose estimation.
11724747
Wavelet estimation of fractional Brownian motion embedded in a noisy environment
This correspondence proposes a wavelet-based fractional Brownian motion (fBm) signal estimation scheme. Despite the fact that wavelet transform approximately whitens the fBm processes, it is observed that statistical dependencies still exist across adjacent wavelet scales and between neighboring wavelet coefficients. These dependencies can be exploited to improve the estimation of fBm signals embedded into noise. The idea is to reorganize the wavelet coefficients into a scale-time mixture model and then carry out the minimum mean-square-error estimation (MMSE) using the model. Experiments show that the proposed scheme obtains better estimates than Wornell and Oppenheim's algorithm, in which the wavelet dependencies are not utilized.
17414114
On the application of cross correlation function to subsample discrete time delay estimation
Cross correlation function (CCF) of signals is an important tool of multi-sensors signal processing. Parabola functions are commonly used as parametric models of the CCF in time delay estimation. The parameters are determined by fitting samples near the maximum of the CCF to a parabola function. In this paper we analyze the CCF for the stationary processes of exponential auto-correlation function, with respect to two important types of sensor sampling kernels. Our analysis explains why the parabola is an acceptable model of CCF in estimating the time delay. More importantly, we demonstrate that the Gaussian function is a better and more robust approximation of CCF than the parabola. This new approximation approach leads to higher precision in time delay estimation using the CCF peak locating strategy. Simulations are also carried out to evaluate the performance of the proposed estimation method for different sample window sizes and signal to noise ratios. The new method offers significant improvement over the current parabola based method.