性能测试论文性能测试论文
银纳米粒子的制备及其能测试新
银纳米粒子的制备及其能测试新毕业论文论文题目：银纳米粒子的制备及其性能测试目录一、前言 (1)1.1纳米粒子概述 (1)1.2 纳米粒子的应用 (1)1.3银纳米粒子概述 (2)1.4 银纳米粒子的制备方法 (3)1.5 研究现状 (3)1.6 研究内容 (4)二、实验部分 (5)2.1 实验药品 (5)2.2 实验仪器 (5)2.3 实验步骤 (6)2.3.1 银纳米粒子的制备 (6)2.3.2 银纳米粒子的表征 (6)2.3.3 银纳米粒子的电催化活性测试 (6)3.1 X射线衍射仪表征 (7)3.3 纳米激光粒度仪测试 (11)3.4 银纳米粒子的电催化活性测试结果 (12)四、实验结论 (13)致谢 (14)参考文献 (15)摘要：随着科学技术的进步，银纳米粒子的研究开发也是日新月里的发展起来了。
本文尝试了一种制备方法：用电化学还原法，以柠檬酸作为配位剂用电化学工作溶液制得银纳米粒子。
用扫描电镜观察所制得站在一定电流、时间内电解AgNO3的产品形貌状态，为松针状的晶体粒子，其粒径在50-100 nm之间，用X射线衍射仪分析了银纳米粒子的晶体结构及样品纯度，纳米粒度分布仪测试得出粒子的大小分布在125-199 nm范围内，并用制得的银纳米粒子修饰碳糊电极，测其C-V 曲线，对其电催化活性进行了初步探索。
关键词：银纳米粒子；电解；制备；表征Abstract: With the progress of science and technology, the research and development of silver nanoparticles also developed very quickly. This paper attempts a preparation method:electricity chemical reduction method, using citric acid as complexing agent chemical workstation in a certain current, time electrolytic AgNO3solution obtained dendritic silver ing scanning electron microscope observed the product appearance, and it shows pine needle shaped crystal particles, the particle diameter between 50-100 nm, by X ray diffraction analysis the silver nanoparticles on the crystal structure and purity of the samples, nanoparticle size distribution tester that particle size distribution in the range of 125-199nm, and the prepared silver nanoparticles modified carbon paste electrode, measured C-V curve, to conduct a preliminary study of the electrocatalytic activity.Key words: silver nanoparticles;Electrolysis; preparation; characterization一、前言1.1纳米粒子概述进入21世纪纳米技术飞速发展，已成为一门新兴产业。
低水平α、β放射性测量仪器计量性能的测试分析
低本底a、卩测量仪 流气正比计数器总a、总卩测量仪
表2实验用仪器的型号与数量
型号
BH1227
LB4008
数量
85道
48道
型号
JC-LSC-2
HY-3322
数量
2道
6道
型号
LB770
MPC-9604
数量
80道
129道
RJ41-4 24道 NMS-21D 2道 LB41-PF3 36道
FYFS-400X 36道
2实验结果与分析 2.1实验数据
10种型号低本底a、卩测量仪的共计232个探
测器（测量通道）各参数的测量结果见表3, 5种型 号流气正比计数器总a、总卩测量仪的共计267个
探测器（测量通道）各参数的测量结果见表4
表3低本底a、卩测量仪性能参数测量结果
计量性能参数 本底 /min~1cm~2‘ 探测效率/% 串道比/%
2 ）探测效率
将a （或B ）标准平面源放置于样品盘中心，使 源表面尽量接近但不超过样晶盘的上沿，固定平面
源。设置仪器的测量次数及单次测量时间,按照式（2 ） 计算仪器的探测效率
Na(B)
~T 1 a(P)
T
1 Oa(P)
〃a(0)
x 100%
(2)
式中：“讪----a （或|3 ）累计计数； ——测量a （或卩）源的累计时间，min;
Aa（p）—— a （或卩）标准平面源在测量时的表
面发射率,（min • 27lsr ） 3）串道比 根据1.2.2测量数据，按式（3 ）计算a射线对
卩道的串道比九讣以及B射线对a道的串道比
式中：M一阻-q------ 测量a（ B）标准平面源时,卩（a） 道的计数；
机械工程测试技术论文
机械工程测试技术论文引言机械工程是一门应用科学，涵盖了许多领域，如动力学、力学、材料科学等。
在机械工程领域中，测试技术起着重要的作用。
本论文将探讨机械工程测试技术的发展、应用以及相关挑战。
发展历程机械工程测试技术的发展经历了多个阶段。
最初的阶段是基于实验的测试，通过搭建实验设备进行物理量的测量。
随着计算机技术的发展，数字化测试逐渐取代了传统的实验方法。
现代机械工程测试技术充分利用了计算机的强大计算能力和数据处理能力，并借助传感器和数据采集系统开展实时数据采集和分析。
应用领域机械工程测试技术广泛应用于以下领域：1.材料测试材料的物理力学特性是机械结构设计的重要参数。
通过使用机械工程测试技术，可以对材料的强度、韧性和疲劳寿命等进行准确测量和分析。
这为工程师提供了可靠的材料数据，有助于设计出更耐用、更安全的机械结构。
2.结构测试机械结构的测试是评估其性能和可靠性的重要手段。
通过应用机械工程测试技术，可以验证设计理论和模型的正确性，并提供改进设计的指导。
结构测试包括静态加载试验、动态响应分析等，旨在评估结构的强度、刚度和稳定性。
3.振动与噪音测试振动与噪音是机械系统中常见的问题，对机器性能和使用寿命产生重要影响。
机械工程测试技术可以用于测量机械系统的振动幅值、频率和振动模式等，并分析其对结构和性能的影响。
同时，噪音测试也是机械工程测试的重要内容，可以用于评估噪音水平，并提供相应的噪音控制建议。
4.流体力学测试流体力学在机械工程中有着广泛的应用，如气动力学、液压学等。
机械工程测试技术在流体力学领域中起着至关重要的作用。
通过测量流体力学参数，如流速、压力和温度等，可以评估流体系统的性能，并提供优化设计的依据。
相关挑战机械工程测试技术虽然已经取得了许多成果，但仍然面临一些挑战。
1.复杂性机械系统的测试涉及到多个物理量的测量和分析，这增加了测试的复杂性。
对于大型和复杂的机械系统，测试过程中需要克服许多技术难题，如数据采集、传感器布置和信号处理等。
【精编范文】论文测试用例-范文模板 (15页)
本文部分内容来自网络整理，本司不为其真实性负责，如有异议或侵权请及时联系，本司将立即删除！== 本文为word格式，下载后可方便编辑和修改！ ==论文测试用例篇一：软件测试用例设计论文浅谈软件测试用例设计摘要软件测试是保证软件产品质量的一个重要因素，而测试用例是实现软件测试的关键，是测试发现错误的必要文档。
从测试用例概述，测试用例的重要性，以及如何设计软件测试用例来介绍它的一种使用方法。
关键词软件测试测试用例测试用例设计一、测试用例概述软件测试是软件生命周期中的一个重要阶段，它是软件品质得以保证的重要过程，是根据软件开发各阶段的规格说明和程序的内部结构而精心设计的一批测试用例，并利用这些测试用例运行软件测试，以发现软件错误的过程。
测试用例是软件质量保证的重要子域。
测试用例（testcase）是关于具体测试步骤的文档，它描述了测试的输入参数、条件及配置、预期的输出结果等，以判断被测软件的工作是否正常。
从表现形式上而言，测试用例可以是纯文本的说明文档，也可以是用脚本语言或高级语言编写的一段程序代码。
二、测试用例的重要性随着中国软件业的日益壮大和逐步走向成熟，软件测试也在不断发展，从最初的由软件编程人员兼职测试到软件公司组建，成立独立专职测试部门，测试工作也从简单测试演变为：编制测试计划、编写测试用例、准备测试数据、编写测试脚本、实施测试、测试评估等多项内容的正规测试。
测试方式则由单纯手工测试发展为手工、自动兼之，并有向第三方专业测试公司发展的趋势。
因此在测篇二：测试论文之《测试用例》测试用例发布日期： 8/19/201X | 更新日期：8/19/201XMicrosoft Corporation内容：讨论 Offline Application Block 的测试方法。
本页内容功能测试白盒测试安全性测试性能测试集成测试内容测试安装测试附录 A 说明了针对 Offline Application Block 运行以确保其正常工作的测试。
系统分析师论文：系统测试
2019年9月，我所在公司承担了某市农村土地承包信息管理系统的开发工作，我有幸作为该项目的技术负责人参与整个开发过程，并负责了该项目的需求分析与系统设计的工作。
项目为该市三区一县的农户提供一个土地承包经营的平台，平台系统由发包方管理，承包方管理，土地信息管理，承包经营管理，数据统计，数据维护等六大功能模块组成。
本文以该系统为例，主要论述了软件系统测试技术在该项目中的具体应用，通过采用功能分解、等价类划分及边界值分析来完成系统的功能测试，通过验证界面和原型的匹配以及浏览器的兼容性来完成界面测试，通过采用 LoadRunner性能测试软件，利用逐渐增加阈值量的方式来完成性能测试。
通过以上测试技术的运用，大大提高了系统的稳定性及可靠性，最终项目顺利上线。
绘宇智能公司是从事土地测绘以及农村土地承包经营权信息采集的企业，在2019年9月委托我方公司开发某市农村土地承包信息管理系统，系统为该市三区一县的农户提供一个土地承包经营的平台。
发包方，一般是土地所有权的村集体，也可以是土地的原承包方，其负责人可以把闲置的土地资源发布出来，有土地经营需要的人士（承包方）可以在平台上选择合适的土地进行经营。
系统由发包方管理，承包方管理，土地信息管理，承包经营管理，数据统计，数据维护等六大功能模块组成，整个开发过程历时一年。
由于使用系统的人员较多，因此，如何提高系统的稳定性及可靠性，软件测试方法的运用显得至关重要。
软件测试是软件交付客户前必须要完成的重要步骤之一，目前仍是发现软件错误或者缺陷的主要手段。
系统测试是将已经确认的软件、计算机硬件、外设、网络等元素结合在一起，针对整个系统进行的测试，目的是验证系统是否满足了需求规格的定义，找出与需求规格不符或者矛盾的地方，从而提出更加完善的方案。
系统测试的主要内容包括功能测试、健壮性测试、性能测试、用户界面测试、安全性测试、安装与反安装测试等。
功能测试主要使用的是黑盒测试方法，目的是验证系统是否达到了用户提出的需求或者隐性的需求；用户界面测试的目的是验证系统的界面设计是否达到客户的要求以及验证浏览器的兼容性；性能测试是系统在一定负载的情况下表现出来的性能是否达到客户的性能指标，同时发现系统中的性能瓶颈，并优化软件最终达到优化系统的目的。
计算机软件测试论文2300字_计算机软件测试毕业论文范文模板
计算机软件测试论文2300字_计算机软件测试毕业论文范文模板计算机软件测试论文2300字(一): 计算机软件的测试技术摘要目前, 在我国经济实力迅速发展的同时, 我国的高新技术产业也在逐步推动我国社会生活的进步。
基于我国计算机技术起步晚的先天不足条件, 改进计算机软件测试技术成为了重中之重。
一方面, 它使得计算机软件工作更加科学准确；另一方面, 它在一定程度上提高了计算机的工作效率。
本文将从我国的计算机软件测试技术研究的概况开始, 深入分析计算机软件测试技术的测试方法以及测试流程。
【关键词】计算机软件测试方法技术策略1对我国计算机软件测试技术研究的概况分析1.1计算机软件测试技术的内涵受到我国历史原因的影响, 计算机软件技术在二十世纪九十年代前后才开始正式引入我国, 远远晚于部分发达国家。
也因我国在当时经济、经济及文化各方面都有较为全面的发展, 基本上在二十一世纪之后, 计算机硬件设施就已经在我国开始普及。
作为一个高新技术产业, 计算机软件行业以前所未有的发展趋势在我国掀起了一阵热潮, 也随之产生了一个新型技术, 计算机软件测试技术。
毕竟计算机软件在我国发展时间并不长, 是一个年轻的产业, 在产品的质量上面很难有一个完全的保障, 对于用户的需求也不能够很好的掌握, 由此引发的种种失误和漏洞也会降低用户对计算机软件的不信任。
这对计算机软件行业的发展来说并不是好的现象。
计算机软件测试技术就是专门为此而产生的, 它采用一些相应并且科学的检测技术, 在使用计算机软件的过程中发现问题并加以解决。
不仅强有力地保证了产品的质量, 降低了产品的后期维修费用, 而且在很大程度上提高了用户的使用体验。
1.2计算机软件测试技术出现的原因在古代, 一个国家是否强大取决于该国的经济地位和军事力量。
而在当今的世界格局, 经济和军事固然不可忽视, 不过最为重要的还是科技力量, 而计算机软件技术的发展则有效地提升了一个国家的科技力量。
【计算机专业文献翻译】性能测试方法
届毕业设计（论文）英文参考文献英文文献1：Database Security文献出处，年，Vol.卷(期) Network Security Volume: 2003, Issue: 6, June, 2003, pp. 11-12作者： Paul Morrison英文文献2：APPROACHES TO PERFORMANCE TESTING文献出处，年，Vol.卷(期)Approaches to Performance Testing Vol.18, No.3, pp.312-319,2000作者： Matt Maccaux学生院系专业名称学生班级学生学号学生姓名学生层次APPROACHES TO PERFORMANCE TESTINGby Matt Maccaux09/12/2005AbstractThere are many different ways to go about performance testing enterprise applications, some of them more difficult than others. The type of performance testing you will do depends on what type of results you want to achieve. For example, for repeatability, benchmark testing is the best methodology. However, to test the upper limits of the system from the perspective of concurrent user load, capacity planning tests should be used. This article discusses the differences and examines various ways to go about setting up and running these performance tests.IntroductionPerformance testing a J2EE application can be a daunting and seemingly confusing task if you don't approach it with the proper plan in place. As with any software development process, you must gather requirements, understand the business needs, and lay out a formal schedule well in advance of the actual testing. The requirements for the performance testing should be driven by the needs of the business and should be explained with a set of use cases. These can be based on historical data (say, what the load pattern was on the server for a week) or on approximations based on anticipated usage. Once you have an understanding of what you need to test, you need to look at how you want to test your application.Early on in the development cycle, benchmark tests should be used to determine if any performance regressions are in the application. Benchmark tests are great for gathering repeatable results in a relatively short period of time. The best way to benchmark is to change one and only one parameter between tests. For example, if you want to see if increasing the JVM memory has any impact on the performance of your application, increment the JVM memory in stages (for example, going from 1024 MB to 1224 MB, then to 1524 MB, and finally to 2024 MB) and stop at each stage to gather the results and environment data, record this information, and then move on to the next test. This way you'll have a clear trail to follow when you are analyzing the results of the tests. In the next section, I discuss what a benchmark test looks like and the best parameters for running these tests.Later on in the development cycle, after the bugs have been worked out of the application and it has reached a stable point, you can run more complex types of tests to determine how the system will perform under different load patterns. These types of tests are called capacity planning, soak tests, and peak-rest tests, and are designed to test "real-world"-type scenarios by testing the reliability, robustness, and scalability of the application. The descriptions I use below should be taken in the abstract sense because every application's usage pattern will be different. For example, capacity-planning tests are generally used with slow ramp-ups (defined below), but if your application sees quick bursts of trafficduring a period of the day, then certainly modify your test to reflect this. Keep in mind, though, that as you change variables in the test (such as the period of ramp-up that I talk about here or the "think-time" of the users) the outcome of the test will vary. It is always a good idea to run a series of baseline tests first to establish a known, controlled environment to compare your changes with later.BenchmarkingThe key to benchmark testing is to have consistently reproducible results. Results that are reproducible allow you to do two things: reduce the number of times you have to rerun those tests; and gain confidence in the product you are testing and the numbers you produce. The performance-testing tool you use can have a great impact on your test results. Assuming two of the metrics you are benchmarking are the response time of the server and the throughput of the server, these are affected by how much load is put onto the server. The amount of load that is put onto the server can come from two different areas: the number of connections (or virtual users) that are hitting the server simultaneously; and the amount of think-time each virtual user has between requests to the server. Obviously, the more users hitting the server, the more load will be generated. Also, the shorter the think-time between requests from each user, the greater the load will be on the server. Combine those two attributes in various ways to come up with different levels of server load. Keep in mind that as you put more load on the server, the throughput will climb, to a point.Figure 1. The throughput of the system in pages per second as load increases over timeNote that the throughput increases at a constant rate and then at some point levels off.At some point, the execute queue starts growing because all the threads on the server will be in use. The incoming requests, instead of being processed immediately, will be put into a queue and processed when threads become available.Figure 2. The execute queue length of the system as load increases over timeNote that the queue length is zero for a period of time, but then starts to grow at a constant rate. This is because there is a steady increase in load on the system, and although initially the system had enough free threads to cope with the additional load, eventually it became overwhelmed and had to start queuing them up.When the system reaches the point of saturation, the throughput of the server plateaus, and you have reached the maximum for the system given those conditions. However, as server load continues to grow, the response time of the system also grows even as the throughput plateaus.Figure 3. The response times of two transactions on the system as load increases over timeNote that at the same time as the execute queue (above) starts to grow, the response time also starts to grow at an increased rate. This is because the requests cannot be served immediately.To have truly reproducible results, the system should be put under a high load with no variability. To accomplish this, the virtual users hitting the server should have 0 seconds of think-time between requests. This is because the server is immediately put under load and will start building an execute queue. If the number of requests (and virtual users) is kept consistent, the results of the benchmarking should be highly accurate and very reproducible.One question you should raise is, "How do you measure the results?" An average should be taken of the response time and throughput for a given test. The only way to accurately get these numbers though is to load all the users at once, and then run them for a predetermined amount of time. This is called a "flat" run.Figure 4. This is what a flat run looks like. All the users are loaded simultaneously.The opposite is known as a "ramp-up" run.Figure 5. This is what a ramp-up run looks like. The users are added at a constant rate (x number per second) throughout the duration of the test.The users in a ramp-up run are staggered (adding a few new users every x seconds). The ramp-up run does not allow for accurate and reproducible averages because the load on the system is constantly changing as the users are being added a few at a time. Therefore, the flat run is ideal for getting benchmark numbers.This is not to discount the value in running ramp-up-style tests. In fact, ramp-up tests are valuable for finding the ballpark in which you think you later want to run flat runs. The beauty of a ramp-up test is that you can see how the measurements change as the load on the system changes. Then you can pick the range you later want to run with flat tests.The problem with flat runs is that the system will experience "wave" effects.Figure 6. The throughput of the system in pages per second as measured during a flat runNote the appearance of waves over time. The throughput is not smooth but rather resembles a wave pattern.This is visible from all aspects of the system including the CPU utilization.Figure 7. The CPU utilization of the system over time, as measured during a flat runNote the appearance of waves over a period of time. The CPU utilization is not smooth but rather has very sharp peaks that resemble the throughput graph's waves.Additionally, the execute queue experiences this unstable load, and therefore you see the queue growing and shrinking as the load on the system increases and decreases over time.Figure 8. The execute queue of the system over time as measured during a flat runNote the appearance of waves over time. The execute queue exactly mimics the CPU utilization graph above.Finally, the response time of the transactions on the system will also resemble this wave pattern.Figure 9. The response time of a transaction on the system over time as measured during a flat runNote the appearance of waves over time. The transaction response time lines up with the above graphs, but the effect is diminished over time.This occurs when all the users are doing approximately the same thing at the same time during the test. This will produce very unreliable and inaccurate results, so something must be done to counteract this. There are two ways to gain accurate measurements from these types of results. If the test is allowed to run for a very long duration (sometimes several hours, depending on how long one user iteration takes) eventually a natural sort of randomness will set in and the throughput of the server will "flatten out." Alternatively, measurements can be taken only between two of the breaks in the waves. The drawback of this method is that the duration you are capturing data from is going to be short.Capacity PlanningFor capacity-planning-type tests, your goal is to show how far a given application can scale under a specific set of circumstances. Reproducibility is not as important here as in benchmark testing because there will often be a randomness factor in the testing. This is introduced to try to simulate a more customer-like or real-world application with a real user load. Often the specific goal is to find out how many concurrent users the system can support below a certain server response time. As an example, the question you may ask is, "How many servers do I need to support 8,000 concurrent users with aresponse time of 5 seconds or less?" To answer this question, you'll need more information about the system.To attempt to determine the capacity of the system, several factors must be taken into consideration. Often the total number of users on the system is thrown around (in the hundreds of thousands), but in reality, this number doesn't mean a whole lot. What you really need to know is how many of those users will be hitting the server concurrently. The next thing you need to know is what the think-time or time between requests for each user will be. This is critical because the lower the think-time, the fewer concurrent users the system will be able to support. For example, a system that has users with a1-second think-time will probably be able to support only a few hundred concurrently. However, a system with a think-time of 30 seconds will be able to support tens of thousands (given that the hardware and application are the same). In the real world, it is often difficult to determine exactly what the think-time of the users is. It is also important to note that in the real world users won't be clicking at exactly that interval every time they send a request.This is where randomization comes into play. If you know your average user has a think-time of 5 seconds give or take 20 percent, then when you design your load test, ensure that there is 5 seconds +/- 20 percent between every click. Additionally, the notion of "pacing" can be used to introduce more randomness into your load scenario. It works like this: After a virtual user has completed one full set of requests, that user pauses for either a set period of time or a small, randomized period of time (say, 2 seconds +/- 25 percent), and then continues on with the next full set of requests. Combining these two methods of randomization into the test run should provide more of a real-world-like scenario.Now comes the part where you actually run your capacity planning test. The next question is, "How do I load the users to simulate the load?" The best way to do this is to try to emulate how users hit the server during peak hours. Does that user load happen gradually over a period of time? If so, a ramp-up-style load should be used, where x number of users are added ever y seconds. Or, do all the users hit the system in a very short period of time all at once? If that is the case, a flat run should be used, where all the users are simultaneously loaded onto the server. These different styles will produce different results that are not comparable. For instance, if a ramp-up run is done and you find out that the system can support 5,000 users with a response time of 4 seconds or less, and then you follow that test with a flat run with 5,000 users, you'll probably find that the average response time of the system with 5,000 users is higher than 4 seconds. This is an inherent inaccuracy in ramp-up runs that prevents them from pinpointing the exact number of concurrent users a system can support. For a portal application, for example, this inaccuracy is amplified as the size of the portal grows and as the size of the cluster is increased.This is not to say that ramp-up tests should not be used. Ramp-up runs are great if the load on the system is slowly increased over a long period of time. This is because the system will be able to continually adjust over time. If a fast ramp-up is used, the system will lag and artificially report a lower response time than what would be seen if a similar number of users were being loaded during a flat run.So, what is the best way to determine capacity? Taking the best of both load types and running a series of tests will yield the best results. For example, using a ramp-up run to determine the range of users that the system can support should be used first. Then, once that range has been determined, doing a series of flat runs at various concurrent user loads within that range can be used to more accurately determine the capacity of the system.Soak TestsA soak test is a straightforward type of performance test. Soak tests are long-duration tests with a static number of concurrent users that test the overall robustness of the system. These tests will show any performance degradations over time via memory leaks, increased garbage collection (GC), or other problems in the system. The longer the test, the more confidence in the system you will have. It is a good idea to run this test twice—once with a fairly moderate user load (but below capacity so that there is no execute queue) and once with a high user load (so that there is a positive execute queue).These tests should be run for several days to really get a good idea of the long-term health of the application. Make sure that the application being tested is as close to real world as possible with a realistic user scenario (how the virtual users navigate through the application) testing all the features of the application. Ensure that all the necessary monitoring tools are running so problems will be accurately detected and tracked down later.Peak-Rest TestsPeak-rest tests are a hybrid of the capacity-planning ramp-up-style tests and soak tests. The goal here is to determine how well the system recovers from a high load (such as one during peak hours of the system), goes back to near idle, and then goes back up to peak load and back down again.The best way to implement this test is to do a series of quick ramp-up tests followed by a plateau (determined by the business requirements), and then a dropping off of the load. A pause in the system should then be used, followed by another quick ramp-up; then you repeat the process. A couple things can be determined from this: Does the system recover on the second "peak" and each subsequent peak to the same level (or greater) than the first peak? And does the system show any signs of memory or GC degradation over the course of the test? The longer this test is run (repeating the peak/idle cycle over and over), the better idea you'll have of what the long-term health of the system looks like.ConclusionThis article has described several approaches to performance testing. Depending on the business requirements, development cycle, and lifecycle of the application, some tests will be better suited than others for a given organization. In all cases though, you should ask some fundamental questions before going down one path or another. The answers to these questions will then determine how to best test the application.These questions are:∙How repeatable do the results need to be?∙How many times do you want to run and rerun these tests?∙What stage of the development cycle are you in?∙What are your business requirements?∙What are your user requirements?∙How long do you expect the live production system to stay up between maintenance downtimes?∙What is the expected user load during an average business day?By answering these questions and then seeing how the answers fit into the above performance test types, you should be able to come up with a solid plan for testing the overall performance of your application. Additional Reading∙WebLogic Server Performance and Tuning - WebLogic Server product documentation∙WebLogic Server performance tools and information - WebLogic Server product documentation ∙The Grinder: Load Testing for Everyone by Philip Aston (dev2dev, November 2002)∙Performance Tuning Guide - WebLogic Portal product documentation∙dev2dev WebLogic Server Product Center性能测试方法对于企业应用程序，有许多进行性能测试的方法，其中一些方法实行起来要比其他方法困难。
毕业论文 系统测试
毕业论文系统测试毕业论文：系统测试引言：在软件开发过程中，系统测试是不可或缺的一环。
它是验证系统是否符合需求规格的重要手段，能够发现潜在的问题和缺陷，确保软件质量。
本文将探讨系统测试的定义、目标、策略以及常见的测试方法和工具。
一、系统测试的定义系统测试是软件开发中的一种测试方法，用于验证整个系统是否满足需求规格。
它是在完成单元测试和集成测试之后进行的，旨在发现系统中的缺陷和问题，并确保系统的正常运行。
二、系统测试的目标1. 发现潜在的问题和缺陷：通过对系统进行全面的测试，发现可能存在的错误和缺陷，以便及时修复和改进。
2. 确保系统的正确性和稳定性：通过系统测试，验证系统是否按照需求规格进行设计和实现，确保系统的功能正常运行且稳定可靠。
3. 提高系统的可用性和用户满意度：系统测试可以发现用户体验方面的问题，通过及时修复和改进，提高系统的可用性和用户满意度。
4. 确保系统的安全性和数据的完整性：系统测试可以发现系统中可能存在的安全漏洞和数据完整性问题，以便及时加以修复和改进。
三、系统测试的策略1. 黑盒测试：黑盒测试是一种测试方法，只关注系统的输入和输出，不考虑系统内部的实现细节。
通过设计测试用例，验证系统是否按照需求规格进行了正确的处理。
2. 白盒测试：白盒测试是一种测试方法，关注系统的内部实现细节。
通过检查代码和设计文档，设计测试用例，验证系统的每个分支和路径是否都被覆盖到。
3. 灰盒测试：灰盒测试是黑盒测试和白盒测试的结合，既关注系统的输入和输出，也关注系统的内部实现细节。
通过设计测试用例，验证系统的功能和内部逻辑是否正确。
四、常见的系统测试方法和工具1. 功能测试：功能测试是系统测试的一种常见方法，用于验证系统的功能是否按照需求规格进行了正确的实现。
通过设计测试用例，覆盖系统的各个功能模块，验证系统的功能是否正常运行。
2. 性能测试：性能测试是系统测试的一种重要方法，用于验证系统在不同负载下的性能表现。
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论文关键词：web 应用性能测试loadrunner论文摘要：性能测试可以测算出应用系统能够承受的负荷，从而保证系统在实际压力下的正常工作。
本文通过一种能够模拟真实用户实际行为的方法，对应用系统进行性能测试，获取数据进行分析，并对其性能指标进行比较，找到系统瓶颈，从而进行性能优化。
引言World Wide Web简称Web或WWW ，中文名字万维网。
是自20世纪90年代以来最重要的Internet 应用。
作为一种资源的组织和表达机制，Web已成为Internet 最主要的信息传送媒介。
随着Internet 的迅速发展，web应用越来越深入人们的工作和生活。
java自1995年问世以来，由于其简单易学、跨平台、纯面向对象等诸多优点吸引了人们。
因此基于java 的web应用程序得到了广泛的应用。
软件除了能满足用户的需求之外，还要保证各个部分协调有效的运行，发挥整个系统的一体作用，因此软件的性能也是非常重要的。
在保证软件质量的过程中，需要进行各种测试，例如功能测试、性能测试、可用性测试、客户端兼容性测试及安全性测试。
由于基于web的B/S架构的应用程序，客户端只能完成浏览、查询、数据输入等简单功能，绝大部分工作由服务器承担，这使得服务器的负担很重。
因此系统并发的用户数，系统的事务率及响应时间，在基于JA V A的web应用程序尤其重要。
所以本文着重描述其性能测试。
1.软件性能测试软件性能测试是为了描述对象与性能相关的特性并对其进行评价，而实施和执行的一类测试。
性能测试主要检验软件是否达到需求规格说明中规定的各类性能指标，并满足一些性能相关的约束和限制条件。
中国软件评测中心将性能测试概括为三个方面：应用在客户端性能的测试、应用在网络上性能的测试和应用在服务器端性能的测试。
本文主要关注应用在客户端性能的测试。
应用在客户端性能测试的目的是考察客户端应用的性能，测试的入口是客户端。
它主要包括并发性能测试、疲劳强度测试、大数据量测试和速度测试等，其中并发性能测试是重点。
并发性能测试的过程是一个负载测试和压力测试的过程，即逐渐增加负载，直到系统的瓶颈或者不能接受的性能点，通过综合分析交易执行指标和资源监控指标来确定系统并发性能的过程。
负载测试是确定在各种负载下系统的性能，目标是测试当负载逐渐增加时，系统组成部分的相关输出项，例如通过量、响应时间、CUP 负载、内存使用等来决定系统的性能。
负载测试是一个分析软件应用程序和支撑架构、模拟真实环境的使用，从而来确定能够接受的性能过程。
压力测试是通过确定一个系统的瓶颈或者不能接受的性能点，来获得系统能提供的最大服务级别的测试。
并发性能测试的目的主要体现在三个方面：以真实的业务为依据，选择有代表性的、关键的业务操作设计测试案例，以评价系统的当前性能；当扩展应用程序的功能或者新的应用程序将要被部署时，负载测试会帮助确定系统是否还能够处理期望的用户负载，以预测系统的未来性能；通过模拟成百上千个用户，重复执行和运行测试，可以确认性能瓶颈并优化和调整应用，目的在于寻找到瓶颈问题。
2.基于JAV A的WEB应用程序性能测试策略软件测试就是在受控制的条件下对系统或应用程序进行操作并评价操作结果的过程，所谓控制条件应包括正常条件与非正常条件。
对任意一个程序进行的测试，无论使用自动化的测试工具或是手动测试，穷尽测试是不可能。
任何一个经过严格测试的程序，也不能保证其百分之百的正确。
因此，为了较少这种不必要的错误。
测试之前一定先要制定其测试策略，测试计划，选用合适的测试工具，从而设计出高效的测试用例。
只有这样，一个好的测试策略和好的测试计划才能做到事倍功半的作用。
基于JA V A的web应用程序性能测试重点在于并发测试。
并发测试一般使用虚载测试的方法,即通过一个控制器发送测试信息给多个参与测试的主机，在每台机器上模拟多个用户的操作（使用多个进程或线程）向服务器发送用户请求，使系统运行起来。
基于以上的工作原理，其性能测试采用的策略主要有：（1）脚本的个数、负载生成器和每个组中包括的VUSER数为固定数。
（2）脚本的个数、负载生成器和每个组中包括的Vuser 数可以以一定的百分比进行改变。
（3）可以固定虚拟用户数、每秒事务数、每分钟也面数及事务的响应时间。
（4）可以设置脚本的种类（5）设置不同的操作系统，模拟真实的用户现象。
3.性能测试的实现性能测试，大多数的测试只有借助测试工具才能完成。
目前，广泛使用的性能测试工具大多是商业软件，如mercury interactive公司的loadrunner、Astra LoadTest，Compuware 公司的QA Load，IBM Rational公司的TeamTest。
本文选用的用Mercury公司的自动化性能测试工具loadrunner，在Windows XP, My Sql数据库的测试环境下进行性能测试。
（1）性能测试设计Loadrunner是一个可以进行自动化测试执行，并对测试的数据进行分析，从而得到系统瓶颈的行业标准的性能测试解决方案。
有以下三部分组成：VuGen用来录制虚拟用户的脚本。
Controller 用来执行脚本并且对整个测试过程进行监控。
Analysis 提供图和报表来显示测试结果。
具体方法：首先通过使用VuGen（虚拟用户生成器）录制用户在客户端应用程序中执行的典型业务流程来开发Vuser脚本。
VuGen 还可以运行脚本，为了成功的把脚本集成到LoadRunner 方案中，在录制了基本的Vuser脚本以后，还要对脚本进行增强及编辑，设置好运行时环境后，以独立模式运行Vuser脚本。
其次通过Controller从一个单一的控制点简单有效地控制所有的Vuser。
导入测试脚本，在方案（描述测试会话期间发生的事情）中配置好Vuser 的计算机列表、运行Vuser脚本的列表以及在方案执行期间运行的指定数量的Vuser或Vuser组。
执行方案时，Controller将该方案中的每个Vuser分散到负载生成器，负载生成器是执行Vuser脚本，从而使Vuser可以模拟实际用户操作的计算机。
在运行的同时，还可以用LoadRunner 的性能监视器来监视方案的执行。
最后，Analysis 把在方案执行期间，LoadRunner纪录下来的不同负载下的应用程序性能，以图和报表显示出来。
从而可以方便的分析出应用程序的性能。
(2)性能测试数据的准备通过使用最少的硬件资源，为所有VUSER提供一致的、可重复并可度量的负载，像实际用户一样使用开发的应用程序，这是Loadrunner的一大特点及优势。
在提供负载时，除了准备一些比较有代表性的数据，还要注意测试脚本的重用问题。
一个是关联，即通过参数化，来实现测试用例的充分利用。
另一个是ip欺骗因为当运行场景时，虚拟用户使用它们所在的负载生成器的固定的IP 地址。
每个Load Generator 上（同时）运行大量的虚拟用户，这样就造成了大量的用户使用同一IP 同时访问一个网站的情况，这种情况和实际运行的情况不符，并且有一些网站会限制同一个IP 的登陆。
为了更加真实的模拟实际情况，LoadRunner允许运行的虚拟用户使用不同的IP 访问同一网站，这种技术称为“IP 欺骗”.（3）性能测试执行在测试计划、测试环境及测试数据准备好以后就可以进行测试。
Controller 通过远程代理调度程序启动负载生成器计算机上的应用程序。
通过代理Controller和负载生成器互相通信。
运行方案时，Controller指示远程代理调度程序启动LoadRunner代理。
该代理根据从Controller接受到的指令来初始化、运行、暂停和停止各个Vuser。
同时，该代理还将各个Vuaer的状态数据传回Controller。
（4）性能测试评估在应用程序的测试测试结束后，可以对应其各个性能指标来分析系统的可用性。
有许多因素能够影响系统的性能指标，如，测试环境、网络、应用的数据库和中间件的使用及它们之间的关联应用。
其中任何一个环节都可能造成整个系统的可用性。
Loadrunner Controller通过隔离并标识潜在的客户端、网络和服务器瓶颈。
监视负载下的网络和服务器资源，检查出现性能延迟的地方：网络或客户端延迟、CPU 性能、I/O延迟、数据锁定或服务器上的其他问题。
在进行web应用测试中，LOADRUNNER提供的性能指标有每秒点击次数吞吐量每秒HTTP响应数、每秒下载页面数每秒连接数。
同时。
用户通过在LOARUNNER ANAL YSIS中看到这些性能指标的图或报表，很方便的分析各部分的性能状况。
4.总结任何软件的测试结果都不是只与应用程序本身有关，特别是性能测试，还与其测试硬件环境、软件环境、测试方法及测试工具有关，因此在进行测试之前、一定要了解应用程序的使用及运行的约束条件。
Loadrunner Controller通过使用虚拟用户技术来达到并发的目的，这个测试属于黑盒测试，测试人员不需要对程序代码有很深刻的了解。
通过模拟真实用户对系统的访问，可以帮助系统分析员提早发现系统的瓶颈，从而优化各部分的软硬件配置。
参考文献：①张大陆，伟力《基于WEB应用系统的评测方法和技术》计算机工程第29卷第四期。
②陈战华杨斌《Client/Server结构软件的性能测试测试技术》。
③啄木鸟部落《如何选择性能测试工具》。
④中国软件评测中心测试中心《性能——软件测试的重中之中》。
⑤LOADRUNNER使用手册。