Automatic test cases generation from software specifications modules

A new technique is proposed in this paper to extend the Integrated Classification Tree Methodology (ICTM) developed by Chen et al. [13] This software assists testers to construct test cases from functional specifications. A Unified Modelling Language (UML) class diagram and Object Constraint Language (OCL) are used in this paper to represent the software specifications. Each classification and associated class in the software specification is represented by classes and attributes in the class diagram. Software specification relationships are represented by associated and hierarchical relationships in the class diagram. To ensure that relationships are consistent, an automatic methodology is proposed to capture and control the class relationships in a systematic way. This can help to reduce duplication and illegitimate test cases, which improves the testing efficiency and minimises the time and cost of the testing. The methodology introduced in this paper extracts only the legitimate test cases, by removing the duplicate test cases and those incomputable with the software specifications. Large amounts of time would have been needed to execute all of the test cases; therefore, a methodology was proposed which aimed to select a best testing path. This path guarantees the highest coverage of system units and avoids using all generated test cases. This path reduces the time and cost of the testing

New and simple algorithms for stable flow problems

Stable flows generalize the well-known concept of stable matchings to markets in which transactions may involve several agents, forwarding flow from one to another. An instance of the problem consists of a capacitated directed network, in which vertices express their preferences over their incident edges. A network flow is stable if there is no group of vertices that all could benefit from rerouting the flow along a walk. Fleiner established that a stable flow always exists by reducing it to the stable allocation problem. We present an augmenting-path algorithm for computing a stable flow, the first algorithm that achieves polynomial running time for this problem without using stable allocation as a black-box subroutine. We further consider the problem of finding a stable flow such that the flow value on every edge is within a given interval. For this problem, we present an elegant graph transformation and based on this, we devise a simple and fast algorithm, which also can be used to find a solution to the stable marriage problem with forced and forbidden edges. Finally, we study the stable multicommodity flow model introduced by Kir\'{a}ly and Pap. The original model is highly involved and allows for commodity-dependent preference lists at the vertices and commodity-specific edge capacities. We present several graph-based reductions that show equivalence to a significantly simpler model. We further show that it is NP-complete to decide whether an integral solution exists

Three-Dimensional Adaptive Grid Computation with Conservative, Marker-Based Tracking for Interfacial Fluid Dynamics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76614/1/AIAA-2006-1523-676.pd

IRMA via SDN: Intrusion Response and Monitoring Appliance via Software-Defined Networking

Recent approaches to network intrusion prevention systems (NIPSs) use software-defined networking (SDN) to take advantage of dynamic network reconfigurability and programmability, but issues remain with system component modularity, network size scalability, and response latency. We present IRMA, a novel SDN-based NIPS for enterprise networks, as a network appliance that captures data traffic, checks for intrusions, issues alerts, and responds to alerts by automatically reconfiguring network flows via the SDN control plane. With a composable, modular, and parallelizable service design, we show improved throughput and less than 100 ms average latency between alert detection and response.Roy J. Carver FellowshipOpe

Testing Methods of Surficial Sinkhole Identification Using Remotely Sensed Data

Nearly a quarter of all people rely on karst aquifers for drinking water. In the United States, the Safe Water Drinking Act requires a complete assessment of public water systems\u27 vulnerabilities to contamination. As part of that assessment, watershed boundaries must be delineated, while recharge and supply locations identified. In the context of karst aquifers, surficial karst features, such as sinkholes, can act as a point source of direct recharge to karst aquifers and create vulnerabilities to critical drinking water sources. Historical methods of locating these features are inefficient and depend on basic field investigations, resulting in a clear need for advanced identification methods. To this end, this study focuses on developing more efficient identification methods that use remotely sensed data to locate and map surficial karst features that may require protection. Satellite and unmanned aerial vehicle (UAV) data were used to explore the resolution needed to identify surficial karst feature signatures and the most promising methods for analyzing these data. This study\u27s data included red, green, blue, and near-infrared reflectance rasters, thermal mosaics, and digital surface and terrain models. Spectral and thermal properties were used to filter data that could include karst features. Additionally, digital elevation models were used to explore multiple smoothing methods, image differencing, edge detection, terrain curvature, sink location, and watershed delineation. Findings from the different methods were compared to known karst feature locations. Data with a resolution between 0.5 and 2.5 meters per pixel were found to be ideal for most methods tested. However, vegetation removal, followed by a simple interpolation to fill these areas, created data analysis problems and highlighted the need for other data products, such as LiDAR, that provide accurate elevations of terrain shrouded by vegetation. In the end, it was found that edge detection, mapping curvature, and locating of low points (or sinks) via DEM analyses are all promising methods. It was concluded that by combining multiple methods, detailed digital terrain models could accurately locate many surficial karst features

Firm's Network versus Board Members' Network: Who to Appoint?

A crucial question at the center of corporate governance theories and of the literature on social networks alike is the sense of empower of prestige, or influence on the actors of a social network. This paper approaches the possibility of measuring this influence by detecting key individuals who support network dynamics. By means of a study conducted on a sample of CAC 40 directors, it will be shown that the most influential elements are not necessarily the best placed at the beginning. Contrary to all expectations, a dynamics of influence is based on criteria of indispensability to the network that will be presented as an example

On Fully Dynamic Graph Sparsifiers

We initiate the study of dynamic algorithms for graph sparsification problems and obtain fully dynamic algorithms, allowing both edge insertions and edge deletions, that take polylogarithmic time after each update in the graph. Our three main results are as follows. First, we give a fully dynamic algorithm for maintaining a $(1 \pm \epsilon)$-spectral sparsifier with amortized update time $poly(\log{n}, \epsilon^{-1})$. Second, we give a fully dynamic algorithm for maintaining a $(1 \pm \epsilon)$-cut sparsifier with \emph{worst-case} update time $poly(\log{n}, \epsilon^{-1})$. Both sparsifiers have size $n \cdot poly(\log{n}, \epsilon^{-1})$. Third, we apply our dynamic sparsifier algorithm to obtain a fully dynamic algorithm for maintaining a $(1 + \epsilon)$-approximation to the value of the maximum flow in an unweighted, undirected, bipartite graph with amortized update time $poly(\log{n}, \epsilon^{-1})$