Earticle

Software testing is an important part of the software development process, is used to confirm the quality or performance of a program is in line with the development of before have put forward some requirements. Is software testing in software before put into operation, the software requirements analysis, design specification and coding of the final review, is the key step in the software quality assurance. Software testing is to find errors and execution process. Software testing in software life cycle across two stages: usually when writing out after each module is for it to do the necessary tests (called unit tests). Coding and unit test belong to the same phase of the software life cycle. After the end of this stage to all kinds of comprehensive testing of software systems also, this is another independent stage in software life cycle, namely the test phase. Software test information collection under distributing environment is one of most important test manage research, the paper adopt DCOM groupware developing technology, make transfer to different platform and type, and use OOD mode to design a test collection software system.

Large numbers of nodes are often densely deployed to deliver the desired environmental attributes to the sink in Wireless Sensor Networks (WSNs), so there is a high spatial correlation among the readings of close sensor nodes. Given a certain requirement for accuracy, only part of the sensor nodes should be required to transport the data to sink. We proposed an Energy-aware Iterative Sampling Framework (EISF) for data gathering to reduce the total number of transmissions by exploiting the correlation. In our method, all nodes in a WSNs compete for reporting nodes with energy-related probability and each nonreporting node autonomously determines whether its own readings are redundant or not by utilizing the overheard packets transmitted by the nearby reporting nodes for each epoch. The redundant nodes will be put into sleep mode. After a limited number of iterations, our algorithm can select a set of sampling nodes to transport data with accuracy guarantees. The results of simulation experiments using the real data demonstrate that our proposed approach is effective in prolonging the network life.

Like all the other fields of data processing, the modern information systems have integrated the results of the advanced technologies of the last decades. These systems contain implicit data which it will be necessary to extract and exploit, by using data Mining techniques. Mining association rules which trends to find interesting association or correlation relationships among large amounts of data is one of these techniques. It is a two-steps process, the first step finds all frequent itemsets and the second step constructs association rules from these frequent sets. The overall performance of mining association rules is determined by the first step which becomes the focus problem. This step is expensive with high demands for computation and data access. Parallel computing seems to have a natural role to play since parallel computers provides scalability. In this paper, we examine the issue of mining association rules among items in large databases transactions using the algorithm Apriori proposed by Agrawal. In this context, we propose a new parallel version of the Apriori algorithm of Agrawal, that is the main algorithm of each data mining technique. Parallel computing seems to have a natural role to play since parallel computers provides scalability. In fact, our objective of our work is to have an efficient parallel execution time that requires a delicate balance between program granularity and communication latency (synchronization overhead) between the different granules. Unlike previous work on parallelization of specific data mining algorithms, our approaches consist to discover the different granularity levels of parallelism and their impact on the performance. In this paper we focus on task and data parallelism (hybrid approach) under distributed memory. In particular, if communication latency is minimal then fine grain partitioning will yield the best performance. This is the case when data parallelism is used. If communication latency is large (as in a loosely coupled system), then coarse grain partitioning is more appropriate. For the target architecture used in this work (distributed-shared memory).), the problem of load balancing among the nodes becomes a more critical issue in attempts to yield high performance. We have carried out a detailed evaluation of the parallelization techniques and the impact of combining different types of parallelism (task, data and pipeline) on the effectiveness of the system.

The clustering Algorithm is a key technique used to prolong the lifetime of a sensor network by reducing energy consumption. It can prolong the network lifetime and improve scalability. In this paper, we propose a novel hybrid distributed energy-efficient heterogeneous clustered protocol for wireless sensor networks (HDEEHC). The HDEEHC protocol periodically selects cluster heads according to a hybrid of a primary parameter and a secondary parameter. The residual energy and the type of a node is the first parameter in the election of a cluster head, and the proximity to its neighbors or node degree is the second. The nodes which have high initial and residual energy will have more chances to be the cluster-heads than the low-energy nodes. The clustering does not depend on the network topology or size. Finally, the simulation results show that HDEEHC achieves a longer lifetime and more stability than HEED clustering protocols in heterogeneous environments.

The similarity of web queries plays an important role in capturing frequently asked questions, most popular topics of search engine or automatic query expansion. Accurate measurement of similarity between queries is crucial. The paper presents a new model for similarity metric of web queries using user logs and applied it into information retrieval for query expansion. Different from previous works, in the new model not only word form, but also semantic information has been taken into account. Experiments show that using the new model in query expansion actually improved recall of 8.1 percent and precision of 9.2 percent, which indicates the good performance.

With growing sophistication of computer worms, it is very important to detect and prevent the worms quickly and accurately at their early phase of infection. Traditional signature based IDS, though effective for known attacks but failed to handle the zero-day attack promptly. Recent works on polymorphic worms does not guarantee accurate signature in presence of noise in suspicious flow samples. In this paper we propose PolyS, an improved version of Hamsa, a network based automated signature generation scheme to thwart zero-day polymorphic worms. We contribute a novel architecture that reduces the noise in suspicious traffic pool, thus enhancing the accuracy of worm’s signature. Also we propose a signature generation algorithm for successfully matching polymorphic worm payload with higher speed and memory efficiency. Analysis shows that our system is fast, accurate, attack-resilient and capable of generating quality signature with low false positive and false negative.

The excessive vibration of motor support in container crane’s machine room affects lifespan of the structure and driving comfort of the operators. According to the result of the vibration site test, the main causes of the machine room vibration in container crane were found out. In order to reduce the vibration response, based on simplifying the system as a single degree of freedom vibration system model and finite element method, two kinds of rapid vibration analysis software were compiled respectively by using Visual Basic (VB), which can parameterize the motor and its support. Meanwhile, their advantages and disadvantages were compared. By analyzing the effects of different motor support structure layouts on its natural frequency and maximum vibration velocity response, it is concluded that increasing and improving the motor support’s cross-sectional moment of inertia and lateral stiffness can reduce the system vibration response. The rapid analysis software’s computing results are almost the same as ANSYS computing results. Furthermore, motor support structure of the container crane machine room was optimized through the original software. The accordance of vibration test data and analysis results prove the credibility and the reliability of the rapid analysis software and methods.

Test suite reduction aims at improving the effectiveness of testing and cutting down the test cost with the least test cases under the condition of satisfying all testing objectives. This paper proposes a new method for test suite reduction with test requirement partition. First, it gives a partition to the set of all the available test cases based on the test requirements. After that, a test suite by the partition is generated and then a smaller test suite is obtained by further reduction according to the relationship between test requirements and test suites. At last, the experimental result shows that the proposed approach is helpful to generate the reduced test suite, which is used to test all the test requirements sufficiently by comparing with the existed methods, at the cost of a moderate loss in fault detection capability.

Grid computing is a promising technology for future computing platforms and is expected to provide easier access to remote computational resources that are usually locally limited. Scheduling is one of the active research topics in grid environments. The goal of grid task scheduling is to achieve high system throughput and to allocate various computing resources to applications. The Complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. Many different methods have been proposed to solve this problem. Some of these methods are based on heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper, a new heuristic algorithm for scheduling meta-tasks in grid computing system is presented which tries to consider the execution time and machine state simultaneously by a mapping function. According to the experimental results, the proposed algorithm confidently demonstrates its competitiveness with previously proposed algorithms.

Islanding detection is one of the essential features for photovoltaic grid-connected systems; detection performance is directly related to safe operation of the equipment. Summarize existing islanding detection, then an active method based on frequency – reactive power feedback islanding detection is proposed. Once an islanding condition occurs, the method introducing a load frequency - reactive power characteristics associated component as input of reactive power perturbation, breaks the existing islands of stability point in the case of the power balance. Then it detects the system frequency deviation by real time, and draws into the function of reactive perturbation to form a positive feedback loop. So the frequency drifting is accelerated and the frequency of the output voltage of the photovoltaic grid-connected inverter exceeds a preset threshold value, and then the islanding will be detected. Simulation shows that the proposed method has no detection dead zone and it is fast and has little influence on power quality.