18609835
Extraction and visualization of architectural structure based on cross references among object files
Reverse engineering of legacy systems is a knowledge-intensive process to reconstruct the understanding of a system. A semi-automatic process that can extract architecture level structure from legacy systems is introduced in This work. Exact facts related to cross-references among ELF objects are extracted from files automatically, and then partitioned into hierarchical groups by close cooperation between domain experts and an assistant tool DEREF. By resolving the cross references among these groups, the architectural structure is reconstructed and then visualized using auto-layout techniques. A case study on three embedded operating system demonstrates that this process can be used to obtain a comprehensive understanding about legacy systems even without any a priori knowledge about its design.
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.
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.
22235359
Towards an Optimization-Based Method for Consolidating Domain Variabilities in Domain-Specific Web Services Composition
In this paper, we put forward an automatic method of acquiring the specific system composition model from a domain composition model and requirements for the specific system in domain-specific Web services composition. This is referred to as the variability consolidation problem in this paper. To achieve this goal, we designed a language to describe domain properties for Web services composition. The basis of our approach is to transform the domain composition model and the requirements for the specific system into a mathematical optimization problem, which can be solved by existing algorithms. Thus, this method is fully automatic and not prone to human errors. Our preliminary experimental results show that our method is quite feasible for solving problems with real world sizes.
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.
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.
15556637
Proximity problems on moving points
A kinetic data structure for the maintenance of a multidimensional range search tree is introduced. This structure is used as a building block to obtain kinetic data structures for two classical geometric proximity problems in arbitrary dlmensions: the first structure maintains the closest pair of a set of continuously moving points, and is provably efficient. The second structure maintains a spanning tree of the moving points whose cost remains within some prescribed factor of the minimum spanning tree.
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.
14086659
VirtualTour: an online travel assistant based on high quality images
With the popularity of both travel and Web, more and more people use online travel services to facilitate their travel activities or share their travel experiences. Considering that existing services emphasize more on the textual content with the pictorial content only as supplement, we propose the VirtualTour system. It is an online travel service dedicated on high quality images, which helps travelers plan their trip. The images of VirtualTour are from photo forum sites. They have rich and accurate metadata which could be used to extract geographic location information of them and assess the quality of them. A representative sights identification algorithm is also proposed to automatically identify the possible related sights of a region. Based on the map services that seamlessly integrated into the system, a wieldy UI is designed to support several useful features, e.g. query by map, location or path.
1688639
IGroup: web image search results clustering
In this paper, we propose, IGroup, an efficient and effective algorithm that organizes Web image search results into clusters. IGroup is different from all existing Web image search results clustering algorithms that only cluster the top few images using visual or textual features. Our proposed algorithm first identifies several query-related semantic clusters based on a key phrases extraction algorithm originally proposed for clustering general Web search results. Then, all the resulting images are separated and assigned to corresponding clusters. As a result, all the resulting images are organized into a clustering structure with semantic level. To make the best use of the clustering results, a new user interface (UI) is proposed. Different from existing Web image search interfaces, which show only a limited number of suggested query terms or representative image thumbnails of some clusters, the proposed interface displays both representative thumbnails and appropriate titles of semantically coherent image clusters. Comprehensive user studies have been completed to evaluate both the clustering algorithm and the new UI.
207066659
Adaptive integration activity management for on demand business process collaboration
Abstract.Today businesses are interacting and collaborating more often in the context of a business value chain involving a variety of partners to deliver products and services. Business to business (B2B) connectivity, Enterprise Application Integration (EAI) and general business collaboration processes are becoming more critical in operating and managing efficient enterprises. The required level of inter and intra enterprise integration is generally a large undertaking and involves substantial development and customization efforts. In this paper, we propose an adaptive integration activity management approach based on web services technologies to reduce code changes associated with business process integration in a distributed collaboration environment. Specifically, we present a concept of activity chain to capture non-deterministic process flows as well as deterministic process flows in a uniform manner. Then an Activity Ontology is introduced to capture the integration requirements that include adaptation behaviors, action properties, business rules, and access control policy references. The unique ontology representation and management of the integration activities provides a uniform way to integrate additional internal and external business applications to reduce the need for pre-defined and hard-wired integration methods and to minimize code changes to existing components in an existing business collaboration and integration (B2B/EAI) infrastructure. In addition, a Petri-Net modeling exercise of the proposed integration activity management is performed to help better understand the actual system and improve the system design beforehand.