18028053
An Experimental Study of Increasing Diversity for Case-Based Diagnosis
Increasing diversity for case-based reasoning (CBR) is an issue that has recently drawn the attention of researchers in the CBR field. Several diversification techniques have been proposed and discussed in the literature. However, whether and to what extent those techniques can bring about benefits to end-users remains in question. In this paper, we report an experiment in applying a diversification technique to a case-based diagnosis tool in a product maintenance domain. The results of this offer some evidence in support of diversification techniques.
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.
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.
6538157
Threshold Tuning for Improved Classification Association Rule Mining
One application of Association Rule Mining (ARM) is to identify Classification Association Rules (CARs) that can be used to classify future instances from the same population as the data being mined. Most CARM methods first mine the data for candidate rules, then prune these using coverage analysis of the training data. In this paper we describe a CARM algorithm that avoids the need for coverage analysis, and a technique for tuning its threshold parameters to obtain more accurate classification. We present results to show this approach can achieve better accuracy than comparable alternatives at lower cost.
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.
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.
9760560
An edge-guided image interpolation algorithm via directional filtering and data fusion
Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts
14923930
Cache Performance of SAT Solvers: a Case Study for Efficient Implementation of Algorithms
We experimentally evaluate the cache performance of different SAT solvers as a case study for the efficient implementation of SAT algorithms. We evaluate several different Boolean Constraint Propagation (BCP) mechanisms and show their respective run time and cache performances on selected benchmark instances. From the experiments we conclude that a cache friendly data structure is a key element in the efficient implementation of SAT solvers. We also show empirical cache miss rates of several modern SAT solvers based on the Davis-Logemann-Loveland (DLL) algorithm with learning and non-chronological backtracking. We conclude that the recently developed SAT solvers are much more cache friendly in data structures and algorithm implementations compared with their predecessors.
14861274
Learning in Region-Based Image Retrieval
In this paper, several effective learning algorithms using global image representations are adjusted and introduced to regionbased image retrieval (RBIR). First, the query point movement technique is considered. By assembling all the segmented regions of positive examples together and resizing the regions to emphasize the latest positive examples, a composite image is formed as the new query. Second, the application of support vector machines (SVM) in relevance feedback for RBIR is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Last, a region re-weighting algorithm is proposed inspired by those feature re-weighting ones. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness of the proposed learning algorithms.
12946117
Searching One Billion Web Images by Content: Challenges and Opportunities
Although content-based image retrieval has been studied for decades, most commercial image search engines are still text-based. However, there is a growing demand for techniques to support content-based image search at Web scale. In this paper, we propose an ambitious goal to searching one billion Web images by content, and discuss the major challenges and opportunities. We also present several important applications that can be greatly benefited by techniques to enable Web-scale image search by content. These applications include image copyright infringement detection, street-side photo search, and searchbased image annotation. We believe that the insights presented in the paper are enlightening to researchers in this field, and any breakthrough we make in this space will lead to many impactful applications in the future.
7654369
Extension of a list coloring problem
Abstract For a graph H , f ( H ) is the smallest integer k such that the join of H with an empty graph E k of order k is not | V ( H ) | -choosable. It was conjectured that for a triangle-free graph G , f ( G ) = n 2 μ ( G ) n n − 2 μ ( G ) , where n = | V ( G ) | and μ ( G ) is the cardinality of a maximum matching of graph G [S. Gravier, F. Maffray, B. Mohar, On a list-coloring problem, Discrete Math. 268 (2003) 303–308]. We verify this conjecture in the case of forests, and propose some related problems.