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牛客网校招面试题库(附答案与解析)测试篇

于 2020-06-02 发布
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COM牛客网一一互联网学习求职必备神器名企校招历年笔试面试真题,尽在牛客网软件测试基础知识1、请你分别介绍一下单元测试、集成测试、系统测试、验收测试、回归测试考点:测试参考回答:单元测试:完成最小的软件设计单元(模块)的验证工作,日标是确保模垬被正确的编码,使用过程设计描述作为指南,对重要的控制跻径诖行测试以发现模玦内的错误,通常情况下是白盒的,对代码风格和规则、程序设计和结构、业务逻辑等进行静态测试,及早的发现和解决不易显现的错误。2、集成测试:通过测试发现与模块接口有关的问题。目标是把通过了单元测试的模块拿来,闷造一个在设计屮所描述的稈序结构,应当避免一次性的集成(除非软件规模很小),而采用增量集成自顶向下集成:模块集成的顺序是首先集成主模块,然后按照控制层次结构冋向下进行集成,隶属于主模块的模块按照深度优先或广度优先的方式集成到整个结构中去。自底向上集成:从原子模开始来进行构造和测试,因为模块是自底冋上集成的,进行时要求所有隶属于某个给顶层次的模块总是存在的,也不再有使用稳定测试桩的必要。3、系统测试:是基于系统整体需求说明书的黑盒类测试,应覆盖系统所有联合的部件。系统测试是针对整个产品系统走行的测试,日的是验证系统是否满足了需求规柊的定义,找出与需求规格不相符合或与之矛盾的地方。系统测试的对象不仅仅包括需要测试的产品系统的软件,还要包含软件所依赖的硬件、外设甚至包括某些数据、某些支持软件及其接口等。因此,必须将系统中的软件与各种依赖的资源结合起来,在系统实际运行环境下来进行测试、回归测试:回归测试是指在发生修改之后重新测试先前的测试用例以保证修改的正确性。理论上,软件产生新版本,都需要进行回归测试,验证以前发现和修复的错误是否在新软件版本上再次出现。根据修复好了的缺陷再重新让行测试。回归测试的日的在于验证以前出现过但已经修复好的缺陷不再重新出现。一般指对某已知修正的缺陷再次围绕它原来出现时的步骤重新测试5、验收测试:验收测试是指系统廾发生命周期方法论的一个阶段,这时相关的用户或独立测试人员根据测试计划和结果对系统进行测试和接收。它让系统用户决定是否接收系统。它是项确定产品是否能够满足合同或用户所规定需求的测试。验收测试包括 alpha测试和Bcta测试Alpha测试:是由用户在开发者的场所来进行的,在一个受控的环境中进行。Beta测试:由软件的最终用户在一个或多个用户场所来进行的,开发者通常不在现场,用户记录测试中遇到的问题并报告给开发者,开发者对系统进行最后的修改,并开始准备发布最终的软件。牛客网,数百万大学生都在使用的免费在线学习平台ξ NOWCODER. COM牛客网一一互联网学习求职必备神器名企校招历年笔试面试真题,尽在牛客网2、请你回答一下单元测试、集成测试、系统测试、验收测试、回归测试这几步中最重要的是哪一步考点:测试参考回答这些测试步骤分别在软件开发的不同阶段对软件进行测试,我认为对软件完整功能进行测试的系统测试很重要,因为此时单元测试和集成测试已完成,能够对软件所有功能进行功能测试,能够覆盖系统所冇联合的部件,是针对整个产品系统进行的测试,能够验证系统是否满足了需求规格的定义,因此我认为系统测试很重要。3、请回答集成测试和系统测试的区别,以及它们的应用场景主要是什么?考点:测试参考回答:区别:、计划和用例编制的先后顺序:从V模型来讲,在需求阶段就要制定系统测试计划和用例,HLD的时候倣集成测试计划和用例,有些公司的具体实践不一样,但是顺序肯定是先做系统测试计划用例,再做集成。2、用例的粒度:系统测试用例相对很接近用户接受测试用例,集成测试用例比系统测试用例更详细,而且对于接口部分要重点写,毕竟要集成各个模块或者子系统。3、执行测试的顺序:先执行集成测试,待集成测试出的问题修复之后,再做系统测试应用场景:集成测试:完成单元测试后,各模块联调测试:集屮在各模块的接口是否一致、各模块间的数据流和控制流是否按照设计实现其功能、以及结果的正确性验证等等;可以是整个产品的集成测试,也可以是大模块的集成测试;集成测试主要是针对程序内部结构进行测试,特别是对程序之间的接口进行测试。集成测试对测试人员的编与脚本能力要求比较高。测试方法一般迒用黑盒测试和白盒测试相结合。系统测试:针对整个产品的全面测试,既包含各模块的验证性测试(验证前两个阶段测试的正确性)和功能性(产品提交个用户的功能)测试,又包括对整个产品的健壮性、安全性、可维护性及各种性能参薮的测试。系统测试测试软件《需求规格说明书》中提到的功能是否有遗漏是否正确的实现。做系统测试要严格按照《需求规格说明书》,以它为标准。测试方法一般都使用黑盒测试法。4、请问测试开发需要哪些知识?需要具备什么能力?考点:测试牛客网,数百万大学生都在使用的免费在线学习平台

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  • 凸优化在信号处理与通信中的应用Convex Optimization in Signal Processing and Communications
    凸优化理论在信号处理以及通信系统中的应用 比较经典的通信系统凸优化入门教程ContentsList of contributorspage IxPrefaceAutomatic code generation for real- time convex optimizationJacob Mattingley and stephen Boyd1.1 Introduction1.2 Solvers and specification languages61. 3 Examples121. 4 Algorithm considerations1.5 Code generation261.6 CVXMOD: a preliminary implementation281.7 Numerical examples291. 8 Summary, conclusions, and implicationsAcknowledgments35ReferencesGradient-based algorithms with applications to signal-recoveryproblemsAmir beck and marc teboulle2.1 Introduction422.2 The general optimization model432.3 Building gradient-based schemes462. 4 Convergence results for the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for l1-based regularization problems672.7 TV-based restoration problems2. 8 The source-localization problem772.9 Bibliographic notes83References85ContentsGraphical models of autoregressive processes89Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe3.1 Introduction893.2 Autoregressive processes923.3 Autoregressive graphical models983. 4 Numerical examples1043.5 Conclusion113Acknowledgments114References114SDP relaxation of homogeneous quadratic optimization: approximationbounds and applicationsZhi-Quan Luo and Tsung-Hui Chang4.1 Introduction1174.2 Nonconvex QCQPs and sDP relaxation1184.3 SDP relaxation for separable homogeneous QCQPs1234.4 SDP relaxation for maximization homogeneous QCQPs1374.5 SDP relaxation for fractional QCQPs1434.6 More applications of SDP relaxation1564.7 Summary and discussion161Acknowledgments162References162Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems166Anthony Man-Cho So and Yinyu Ye5.1 Introduction1665.2 Problem formulation1695.3 Analysis of the SDr detector for the MPsK constellations1725.4 Extension to the Qam constellations1795.5 Concluding remarks182Acknowledgments182References189Semidefinite programming matrix decomposition, and radar code design192Yongwei Huang, Antonio De Maio, and Shuzhong Zhang6.1 Introduction and notation1926.2 Matrix rank-1 decomposition1946.3 Semidefinite programming2006.4 Quadratically constrained quadratic programming andts sdp relaxation201Contents6.5 Polynomially solvable QCQP problems2036.6 The radar code-design problem2086.7 Performance measures for code design2116.8 Optimal code design2146.9 Performance analysis2186.10 Conclusions223References226Convex analysis for non-negative blind source separation withapplication in imaging22Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang7.1 Introduction2297.2 Problem statement2317.3 Review of some concepts in convex analysis2367.4 Non-negative, blind source-Separation criterion via CAMNS2387.5 Systematic linear-programming method for CAMNS2457.6 Alternating volume-maximization heuristics for CAMNS2487.7 Numerical results2527.8 Summary and discussion257Acknowledgments263References263Optimization techniques in modern sampling theory266Tomer Michaeli and yonina c. eldar8.1 Introduction2668.2 Notation and mathematical preliminaries2688.3 Sampling and reconstruction setup2708.4 Optimization methods2788.5 Subspace priors2808.6 Smoothness priors2908.7 Comparison of the various scenarios3008.8 Sampling with noise3028. 9 Conclusions310Acknowledgments311References311Robust broadband adaptive beamforming using convex optimizationMichael Rubsamen, Amr El-Keyi, Alex B Gershman, and Thia Kirubarajan9.1 Introduction3159.2 Background3179.3 Robust broadband beamformers3219.4 Simulations330Contents9.5 Conclusions337Acknowledgments337References337Cooperative distributed multi-agent optimization340Angelia Nedic and asuman ozdaglar10.1 Introduction and motivation34010.2 Distributed-optimization methods using dual decomposition34310.3 Distributed-optimization methods using consensus algorithms35810.4 Extensions37210.5 Future work37810.6 Conclusions38010.7 Problems381References384Competitive optimization of cognitive radio MIMO systems via game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. They start by rederiving the gradient method from several different perspectives and suggest a modification that overcomes the slow convergence of the algorithmThey then apply the developed framework to different image-processing problems suchas e1-based regularization, TV-based denoising, and Tv-based deblurring, as well ascommunication applications like source localizationIn Chapter 3, Songsiri, Dahl, and Vandenberghe consider graphical models for autore-gressive processes. They take a parametric approach for maximum-likelihood andmaximum-entropy estimation of autoregressive models with conditional independenceconstraints, which translates into a sparsity pattern on the inverse of the spectral-densitymatrix. These constraints turn out to be nonconvex. To treat them the authors proposea relaxation which in some cases is an exact reformulation of the original problem. 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