Test Case Prioritization in Unit and Integration Testing:A Shuffled-Frog-Leaping Approach

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.

Shuffled frog leaping algorithm with non-dominated sorting for dynamic weapon-target assignment

The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.

Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping-particle swarm optimization convolutional neural network

In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.

融合Shufflenet-V2的Yolov5轻量化目标检测方法

选取目标检测领域中常见的城市道路车辆作为识别目标,针对如今日益增长的目标检测网络模型计算量与移动端设备匮乏的计算资源相冲突的问题,提出了一种将Shuffl enet-V2轻量级神经网络模型融合进Yolov5目标检测框架中的方法,修改网络头部卷积层,加入注意力机制SE-Layer层,并将激活函数修改为H-Swish函数。实验选取UA-DETRAC公开数据集,结果表明,模型大小达到了1.53 M,在同等平均准确率的模型中达到最低,为目标检测模型在计算资源有限的移动端设备中的应用与部署提供了有效的参考。

一种Spark集群下的shuffle优化机制

Spark是基于内存的分布式数据处理框架,其shuffle过程中大量数据需要通过网络传输,已成为Spark最主要的瓶颈之一。针对shuffle过程中存在的数据分布不均造成不同节点网络I/O负载不均的问题,设计了基于task本地性等级的重启策略,进一步提出了均衡的调度策略来平衡各节点的网络I/O负载。最后通过实验验证了优化机制能够减少计算任务的执行时间,提升整个shuffle过程的执行效率。

MapReduce中shuffle优化与重构

详细介绍了MapReduce编程框架,具体分析了MapReduce中shuffle阶段流程。分别从Map端数据压缩、重构远程数据拷贝传输协议、Reduce端内存分配优化三方面来优化和重构Shuffle。最后通过搭建Hadoop集群,运用MapReduce分布式算法测试实验数据。实验结果证明优化重构后的shuffle能显著提高MapReduce计算性能。

MapReduce Shuffle性能改进

针对Reduce端从Map端拷贝大量结果数据耗时长的问题,提出一种新方案即对Map节点上同一个作业的多个Map任务所产生的大量临时结果数据进行合并,以取代原有Map Reduce架构对单个Map任务的结果数据作合并的机制。该方案减少了Map节点的输出结果数据量,能减少整个集群的网络传输数据量,节省Reduce端拷贝Map端输出数据的时间,从而减少了Map Reduce作业执行时间,提升了Map Reduce的执行性能。

iPodshuffle

苹果iPod shuffle数字音乐播放器的体积和-包口香糖大小相当．小巧而轻盈并配有特制的挂绳，打开包装就可以直接随身佩带。iPod shuffle拥有512MB和1GB两种型号。能够容纳120首和240首歌曲。

改进的Spark Shuffle内存分配算法

Shuffle性能是影响大数据集群性能的重要指标,Spark自身的Shuffle内存分配算法试图为内存池中的每一个Task平均分配内存,但是在实验中发现,由于各Task对于内存需求的不均衡导致了内存的浪费和运行效率较低的问题。针对上述问题,提出一种改进的Spark Shuffle内存分配算法。该算法根据Task的内存申请量和历史运行数据将Task按内存需求分为大小两类,对小内存需求型Task作"分割化"处理,对大内存需求型Task基于Task溢出次数和溢出后等待时间分配内存。该算法充分利用内存池的空闲内存,可以在数据倾斜导致的Task内存需求不均衡的情况下进行Task内存分配的自适应调节。实验结果表明,改进后算法较原算法降低了Task的溢出率,减少了Task的周转时间,提高了集群的运行性能。

浓缩的精华——iPod shuffle2数字音乐播放器

iPod经历了一代又一代的革新后,终于将世界上第一台最小的数字音乐播放器展现于世人面前。犹记当年shuffle问世时的火爆场面,可谓迷倒众生,更是创下了1000千万台的惊人销量。而如今,shuffle2再一次给我们惊喜,出色的设计及卓越的时尚气息俘获了年轻人的心,只是这样一个再简单不过的小东西,而它不是别人,正是Apple,正是iPod,正是shuffle,我们有理由相信它将会掀起又一股时尚热潮。

Twinning in Intermetallic Compounds Are Long Shear Vectors and/or Shuffles Really Necessary?

In this paper the geometric description and general theory of mechanical twinning are reviewed, the twins in general lattices and superlattices are summarized, and the kinetic process by which mechanical twins form is revisited. A case study of mechanical twinning of HfV2+Nb, (cubic) Laves phase, is presented and the synchroshear of selected atomic layers is proposed to explain the physical process of twin formation. If the twins form in this way, then long shear vectors and / or atomicshuffles are not really necessary.

Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems

This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.

Improved Shuffled Frog Leaping Algorithm Optimizing Integral Separated PID Control for Unmanned Hypersonic Vehicle

To solve the flight control problem for unmanned hypersonic vehicles,a novel intelligent optimized control method is proposed.A flight control system based on integral separated proportional-integral-derivative(PID)control is designed for hypersonic vehicle,and an improved shuffled frog leaping algorithm is presented to optimize the control parameters.A nonlinear model of hypersonic vehicle is established to examine the dynamic characteristics achieved by the flight control system.Simulation results demonstrate that the proposed optimized controller can effectively achieve better flight control performance than the traditional controller.

NV-Shuffle:基于非易失内存的Shuffle机制

Shuffle是大数据处理过程中一个极为重要的阶段.不同类型的Task(或者Stage)之间通过Shuffle进行数据交换.在Shuffle过程中数据需要进行持久化,以达到避免重计算和容错的目的.因此Shuffle的性能是决定大数据处理性能的关键因素之一.由于传统Shuffle阶段的数据通过磁盘文件系统进行持久化,所以影响Shuffle性能的一个重要因素是I/O开销,尤其是对基于内存计算的大数据处理平台,例如Spark,Shuffle阶段的磁盘I/O可能拖延数据处理的时间.而非易失内存(NVM)具有读写速度快、非易失性以及高密度性等诸多优点,它们为改变大数据处理过程中对磁盘I/O的依赖、克服目前基于内存计算的大数据处理中的I/O性能瓶颈提供了新机会.提出一种基于NVM的Shuffle优化策略——NV-Shuffle.NV-Shuffle摒弃了传统Shuffle阶段采用文件系统的存储方式,而使用类似于Memory访问的方式进行Shuffle数据的存储与管理,避免了文件系统的开销,并充分发挥NVM的优势,从而减少Shuffle阶段的耗时.在Spark平台上实现了NV-Shuffle,实验结果显示,对于Shuffleheavy类型的负载,NV-Shuffle可节省大约10%~40%的执行时间.

Modified Shuffled Frog Leaping Algorithm for Solving Economic Load Dispatch Problem

In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.

Progressive Randomization of a Deck of Playing Cards: Experimental Tests and Statistical Analysis of the Riffle Shuffle

The question of how many shuffles are required to randomize an initially ordered deck of cards is a problem that has fascinated mathematicians, scientists, and the general public. The two principal theoretical approaches to the problem, which differed in how each defined randomness, has led to statistically different threshold numbers of shuffles. This paper reports a comprehensive experimental analysis of the card randomization problem for the purposes of determining 1) which of the two theoretical approaches made the more accurate prediction, 2) whether different statistical tests yield different threshold numbers of randomizing shuffles, and 3) whether manual or mechanical shuffling randomizes a deck more effectively for a given number of shuffles. Permutations of 52-card decks, each subjected to sets of 19 successive riffle shuffles executed manually and by an auto-shuffling device were recorded sequentially and analyzed in respect to 1) the theory of runs, 2) rank ordering, 3) serial correlation, 4) theory of rising sequences, and 5) entropy and information theory. Among the outcomes, it was found that: 1) different statistical tests were sensitive to different patterns indicative of residual order;2) as a consequence, the threshold number of randomizing shuffles could vary widely among tests;3) in general, manual shuffling randomized a deck better than mechanical shuffling for a given number of shuffles;and 4) the mean number of rising sequences as a function of number of manual shuffles matched very closely the theoretical predictions based on the Gilbert-Shannon-Reed (GSR) model of riffle shuffles, whereas mechanical shuffling resulted in significantly fewer rising sequences than predicted.

Hadoop MapReduce与Spark 的Shuffle过程原理

针对大数据分布式编程新知识初学者难以理解的问题,选取Hadoop MapReduce与Spark计算模型中涉及核心环节Shuffle过程原理深入剖析,并以单词词频统计算法Shuffle过程为例。实验结果表明,Spark节省了中间数据排序时间Tsort,消耗了中间数据传输时间 Ttrans,而在中间数据文件数目以及同步次数等方面效率都均要低于MapReduce。通过整体功能、流程实现、数据拉取机制等方面对比分析,帮助初学者深入了解分布式编程的基本思想,降低大数据学习门槛。

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

A ε-indicator-based shuffled frog leaping algorithm for many-objective optimization problems

Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.

把shuffle挂起来：GRIFFIN TuneCaps便携配件

特立独行的你是否已经觉得把iPod shuffle挂在脖子上有些俗气了呢？那么就让我们通过TuneCaps来给“舒服”换个戴法。TuneCaps可以安装在iPod shuffle的USB连接器上．曲别针式的设计可以让用户将MP3别在衣服上。如果你不喜欢这种佩戴方式．可安装挂链的TuneCaps还能给你另一种选择方案。