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自动化系统中的智能决策模型研究在当今科技飞速发展的时代，自动化系统已经广泛应用于各个领域，从工业生产到金融服务，从交通运输到医疗健康。
而智能决策模型作为自动化系统的核心组成部分，对于提高系统的效率、准确性和适应性具有至关重要的作用。
智能决策模型旨在模仿人类的决策过程，通过对大量数据的分析和处理，提取有价值的信息，并据此做出合理的决策。
它能够快速处理复杂的问题，减少人为错误，并且能够在不断变化的环境中实时调整决策策略。
要理解智能决策模型，首先需要了解其数据来源。
这些数据通常来自于多个渠道，包括传感器、数据库、网络等。
例如，在工业生产中，温度、压力、流量等传感器收集的实时数据可以反映生产过程的状态；在金融领域，交易记录、市场行情、宏观经济数据等则是决策的重要依据。
数据收集完成后，需要进行预处理。
这包括数据清洗、转换和集成。
数据清洗旨在去除噪声和异常值，以确保数据的质量；数据转换则将数据转换为适合模型处理的格式；数据集成则是将来自不同来源的数据整合在一起。
在智能决策模型中，常见的方法有基于规则的模型、基于统计的模型和基于机器学习的模型。
基于规则的模型是通过事先定义好的一系列规则来进行决策。
例如，如果某个生产指标超过了设定的阈值，就触发相应的警报或采取特定的控制措施。
这种模型的优点是简单直观，易于理解和解释，但缺点是规则的制定往往依赖于专家经验，难以应对复杂多变的情况。
基于统计的模型则利用统计学原理对数据进行分析和建模。
例如，通过回归分析可以预测未来的销售趋势，通过方差分析可以比较不同生产工艺的效果。
这类模型在处理具有一定规律性的数据时表现出色，但对于非线性和不确定性较强的数据可能效果不佳。
基于机器学习的模型是当前智能决策领域的热门研究方向。
机器学习算法能够自动从数据中学习模式和规律，从而做出决策。
常见的机器学习算法包括决策树、神经网络、支持向量机等。
以决策树为例，它通过对数据的不断分割和分类，形成一棵类似于树状的结构，从而实现决策。
C h i n as t o r a g e&t r a n s p o r t m a g a z i n e 2024.031.自动化仓库货位分配模式改进数学模型设计1.1自动化仓库货位分配影响因素分析仓库中货物空间的分配主要基于随机分配策略，重点考虑存储场所的精准匹配。
利用空间分配算法建立了货物空间分配的优化方法。
出于便于计算的目的，假设：（1）仓库中的三个维度只有一个入口和出口点，每个巷道线宽都是协调一致的。
（2）货物储存在托盘单元中，托盘单元形状是一个规则的矩形，重心位于其几何中心。
设货架层向集合为Q ，且q ∈Q ；货架列向集合为S ，且；存放货物的立体仓库的货架排向集合为R ，且r ∈R 。
共布置有 R 排货架在该立体仓库中，每排货架含 s 列 Q 层；设置（x r s q ，y r s q ，z r s q）为第r 排第s 列第q 层的货位中心坐标（0，0，0）：仓库出入口坐标（0，0，0），第1排、第1列、第1层货架被规定为距离出入口最近的排、列、层。
w 0为巷道的宽度；，l s 为第s 列货架的长度，w r 为第r 排货架的深度，h q 为第q 层货架的高度，!r s q 为其货位位置；（a i ，b i ，c i ）表示第i 件货物的长宽高，周转率为p i ，重量为g i ，仓库内储存的货物集合用I 来表示，i ∈I 。
存储环境的位置临界值为"i 。
当货物储存要求所需位置与实际储存位置精确一致时，应精确计算仓库中每个储存位置的实际位置。
计算过程如下：首先，在立体货架上，第r 排第s 列第q 层的货位与所有位置计距离的集合用D r s q 表示。
将距离从最小到最大进行排序，然后使用货物位置中心附近的前一个位置计算货物的位置。
在此计算的基础上，根据公式（1）确定负载位置中心与最近的仪表之间的距离d ，d r u s ∈D r s q。
d r u s=（x r s q -x ）2+（y r s q -y ）2+（z r s q -z ）2（1）其次，将两点之间位置的影响因素设定为两点之间位置差与两点之间距离的比值，并将从指定位置开始的位置的变化系数设置为所选m 个位置之间m-1位置的平均影响因素。
大模型的应用可以生成自动化用例，这通常涉及到机器学习和人工智能技术的结合。
以下是一些步骤，可以帮助您应用大模型生成自动化用例：1. 确定目标：首先，您需要明确自动化用例的目标和目的。
这包括确定要解决的问题、评估的性能指标以及所需的测试范围。
2. 收集数据：收集与目标相关的数据，包括用户输入、系统输出、异常情况和错误等。
这些数据将成为大模型训练的基础。
3. 选择大模型：根据目标和应用场景，选择适合的大模型，如自然语言处理（NLP）模型、计算机视觉（CV）模型或混合模型。
这些模型通常具有强大的表示能力和泛化能力，能够生成高质量的自动化用例。
4. 准备数据集：将收集的数据集划分为训练集、验证集和测试集。
训练集用于训练模型，验证集用于调整模型参数和评估性能，测试集用于验证模型的泛化能力。
5. 训练模型：使用所选的大模型进行训练。
在此过程中，您可以根据需要对模型进行微调和优化，以提高模型的性能和生成自动化用例的质量。
6. 生成用例：训练完成后，大模型将自动生成与目标相关的自动化用例。
这些用例通常包括预期的输入和输出、异常情况下的应对措施以及错误恢复策略等。
您可以根据需要对生成的用例进行调整和优化。
7. 测试和验证：对生成的自动化用例进行测试和验证，以确保其符合预期并具有实际应用价值。
您可以使用测试数据、人工评估或模拟场景等方法进行测试。
8. 部署和维护：将生成的自动化用例部署到实际应用中，并进行定期维护和更新，以确保其持续有效和安全。
总之，应用大模型生成自动化用例需要充分了解目标、数据、模型选择、数据准备、模型训练、用例生成、测试和验证等步骤。
通过不断优化和调整，您可以获得高质量的自动化用例，从而提高软件质量和可靠性。
MATLAB中常见的自动化建模方法介绍随着科技的不断进步，自动化建模在各个领域中变得越来越重要。
MATLAB作为一种强大的数学建模与仿真工具，为研究人员和工程师们提供了许多自动化建模方法。
本文将介绍几种常见的MATLAB中的自动化建模方法，包括系统辨识、机器学习和优化方法。
一、系统辨识系统辨识是在无法直接获得系统模型的情况下，通过对系统输入和输出数据的观测来估计系统模型。
MATLAB提供了多种用于系统辨识的函数和工具箱，其中最常用的是System Identification Toolbox。
System Identification Toolbox提供了参数估计、模型结构选择和模型验证等功能。
在MATLAB中，使用系统辨识工具箱进行模型辨识一般包括以下步骤：收集系统输入和输出数据、选择适当的模型结构、参数估计和模型验证。
通过这些步骤，研究人员可以获得一个能够准确描述系统动态特性的模型。
二、机器学习机器学习是一种通过让计算机从数据中学习，并且在新的数据上做出预测或决策的方法。
在MATLAB中，有多种机器学习算法可供选择，包括支持向量机（SVM）、人工神经网络（ANN）和决策树等。
支持向量机是一种基于统计学习理论的二分类器，其主要思想是通过在高维特征空间中找到一个最优超平面来实现数据分类。
MATLAB中的Support Vector Machines Toolbox提供了一系列用于支持向量机模型的训练和应用的函数。
人工神经网络是一种模拟人脑神经元网络的算法，它可以通过学习样本数据来进行分类、回归、聚类等任务。
MATLAB中的Neural Network Toolbox提供了一系列用于构建、训练和应用神经网络的函数和工具。
决策树是一种通过对数据进行分割来实现分类的方法。
决策树模型通过一系列的判定条件将数据分为不同的类别。
在MATLAB中，可以利用Classification Learner App来构建和训练决策树模型，同时还可利用TreeBagger函数进行随机森林模型的构建和训练。
自动化测试中的测试金字塔模型自动化测试在软件开发过程中扮演着重要的角色。
它可以提高测试效率、降低成本，并确保软件质量。
而在自动化测试中，测试金字塔模型成为一种被广泛采用的测试策略。
本文将介绍测试金字塔模型的概念、原理以及如何应用于自动化测试中。
1. 测试金字塔模型概述测试金字塔模型是Mike Cohn提出的一种软件测试策略。
它将测试活动分为三个层次：单元测试、集成测试和端到端测试。
这三个层次分别对应金字塔模型的底部、中部和顶部，体现了测试案例的数量和漏斗状递减的关系。
具体来说，金字塔底部的单元测试数目最多，而端到端测试数目最少。
2. 单元测试单元测试是测试金字塔模型的基础层，也是最底部的一层。
它是对软件的最小可测试单元进行测试，例如函数、方法或类等。
单元测试通常由开发人员编写，能够帮助他们验证代码的正确性，并发现和修复潜在的bug。
单元测试应该覆盖尽可能多的代码逻辑，确保代码的可靠性。
3. 集成测试集成测试位于测试金字塔模型的中间层，它的目标是验证不同单元的交互和集成是否正常。
在集成测试中，开发人员将已通过单元测试的模块进行组合，并进行测试。
它可以帮助发现模块间的接口问题、数据传递问题以及组合后的功能是否正常工作。
集成测试可以提前发现整体系统的问题，降低后续测试阶段的风险。
4. 端到端测试端到端测试是测试金字塔模型的顶部层，也是最高级的测试层次。
它是对整个软件系统的功能进行测试，确保系统在各个组件之间的集成和交互正常。
端到端测试是用户视角下的测试，它模拟用户真实使用场景，验证系统的完整性、稳定性和可用性。
5. 测试金字塔模型的优势使用测试金字塔模型进行自动化测试有以下几个优势：- 效率和速度：通过基于金字塔底部的单元测试，可以快速运行大量的测试案例，提高测试效率。
- 可维护性：由于单元测试是针对代码的最小单元进行测试，当代码发生变更时，只需重新运行受影响的单元测试，提高了测试的可维护性。
- 降低成本：通过自动化执行大量的测试案例，可以减少人工测试的工作量，降低了测试成本。
自动化机器学习模型训练自动化机器学习（Auto-ML）是一种将数据科学模型开发的管道组件自动化的技术。
它可自动化各种管道组件，包括数据理解，EDA，数据处理，模型训练，超参数调整等。
对于端到端机器学习项目，每个组件的复杂性取决于项目。
在训练自动化机器学习模型时，可能会采用不同的方法，包括：1.训练-测试集分割：这种方法将可用数据分割成两部分，一部分作为训练集（通常占原始数据的80%），用于建立预测模型，另一部分作为测试集（其余20%的数据），用于评估模型在未见过的数据上的性能。
2.训练-验证-测试集分割：这种方法将数据进一步分割成三部分，包括训练集、验证集和测试集。
训练集用于训练模型，验证集用于调整模型的超参数，测试集用于评估模型的最终性能。
以LazyPredict 为例，这是一个开源的Python 库，可以自动执行模型训练管道并加快工作流程。
它可为分类数据集训练约30个分类模型，为回归数据集训练约40个回归模型。
LazyPredict将返回经过训练的模型以及其性能指标，而无需编写太多代码。
可以轻松比较每个模型的性能指标，并调整最佳模型以进一步提高性能。
为了实现自动化机器学习模型训练，需要以下步骤：1.数据准备：包括数据收集、清洗和处理等，为后续的模型训练提供高质量的数据集。
2.特征工程：通过选择、提取、转换和缩放等手段，将原始数据转化为适合模型训练的特征。
3.模型选择和调参：根据问题的类型和数据的特性，选择合适的机器学习模型，并调整模型的超参数，以获得最佳的模型性能。
4.模型训练：使用训练集对模型进行训练，得到训练好的模型。
5.模型评估：使用测试集对训练好的模型进行评估，通过比较预测结果和实际结果来衡量模型的性能。
6.模型优化：根据评估结果对模型进行调整和优化，以进一步提高模型的性能。
总之，自动化机器学习模型训练是利用自动化技术来简化机器学习模型的开发流程，提高开发效率和质量。
机器学习中的自动化模型选择与调参技巧在机器学习中，模型的选择和调参是非常重要的环节。
随着机器学习的快速发展，越来越多的算法和模型被提出，选择合适的模型和调整模型参数成为了研究者和从业者需要面对的问题。
本文将介绍机器学习中的自动化模型选择与调参技巧，帮助读者更好地进行模型选择和参数调整。
首先，自动化模型选择是指通过算法和工具来自动选择合适的模型。
这种方法可以显著减少人工干预和主观判断带来的不确定性。
常见的自动化模型选择方法有网格搜索、随机搜索和基于模型性能的自适应方法。
网格搜索是一种常见的自动化模型选择方法。
它通过指定一组待调节的超参数，然后在参数空间中进行穷举搜索。
对于每一组参数，都进行交叉验证来评估模型的性能。
网格搜索选择性能最好的参数组合作为最终模型的参数。
尽管网格搜索方法的计算复杂度较高，但由于其可解释性强，仍然是许多研究者和从业者的首选。
与网格搜索相比，随机搜索是一种计算开销相对较小的自动化模型选择方法。
它通过在参数空间中随机选择一组参数进行模型训练和评估。
随机搜索方法减少了所有可能参数组合的搜索，只关注于随机选择的一部分。
这种方法能够在一定程度上加速模型选择过程，并且在某些情况下能够找到更好的参数组合。
除了之前提到的方法，还有一些基于模型性能的自适应方法。
这些方法根据不同模型的性能进行参数选择。
例如，自适应调整学习率的方法可以根据模型在训练过程中的性能动态调整学习率。
这种方法能够在模型训练的过程中不断优化参数，从而提高模型的性能。
在进行自动化模型选择时，还有一些其他的技巧和要点需要考虑。
首先，需要选择合适的评估指标来评估模型的性能。
常见的评估指标包括准确率、精确率、召回率、F1分数等，选择合适的指标能更好地反映模型在具体任务中的性能。
其次，需要注意参数的选择范围。
在进行模型选择时，不能只关注部分参数，而忽视其他参数的影响。
应该尽可能地考察所有可能的参数组合，以了解模型的全局性能。
最后，要进行合理的验证方法来评估模型的性能。
自动化车床管理的数学模型
自动化车床管理的数学模型可以基于以下几个关键指标进行建模和优化：
1. 生产效率：可以使用产量、生产周期、产能利用率等指标来衡量车床的生产效率。
可以使用线性规划或者整数规划模型来优化车床的生产计划，以最大化生产效率。
2. 造成漏产的故障率：可以使用故障率、维修时间、维修费用等指标来衡量车床的可靠性。
可以使用可靠性中心理论来建立车床的可靠性模型，并通过优化维护策略，降低故障率以减少造成漏产的机会。
3. 工具寿命：车床的切削工具寿命对生产效率和可靠性都有重要影响。
可以使用刀具寿命、切削速度、加工质量等指标来衡量工具寿命。
可以使用优化理论和刀具磨损模型，来优化刀具更换策略，最大化工具寿命。
4. 能源消耗：车床的能源消耗对生产成本和环境影响都有重要影响。
可以使用能耗、电费、碳排放等指标来衡量能源消耗。
可以使用线性规划模型来优化能源使用策略，达到节能减排的目标。
5. 人力资源配置：车床的操作人员配置对于生产效率和人力资源利用率都具有重要影响。
可以使用操作人员数量、工作时间、工作强度等指标来衡量人力资源配置。
可以使用排队论模型和资源分配算法来优化人力资源的调度，最大化人力资源的利用
效率。
这些数学模型可以通过数值方法、优化算法等工具来求解，并通过敏感性分析和模拟仿真等方法进行验证和优化。
自动化ROI模型及实践原则目录自动化的价值01测试ROI 金字塔02寻找最优ROI策略03测试脚本如何写04为什么做自动化？举个例子：一个 Web UI 订火车票的软件，成功订一张火车票这个测试案例，要做自动化所花费的成本、还有得到的收益，会是多少呢？手工执行：0.5小时（运行登录->订车票->查数据库）自动化：Selenium脚本8小时。
ROI=产出/投入=0.5/8=0.0625<7%实际上，自动化测试案例开发出来后，肯定不止运行一次的。
多运行一次，就会多节省下来一份工作量，如果用n来指代运行次数，t指代单次测试时间，现在的产出变成了n*t，n越大，产出就会越大。
还有一个重要的点，这个自动化用例是需要维护的，维护算作m，当前的用例还没有被更新过，m=0。
ROI = 0.5*n/8+0。
只要这个selenium脚本运行超过16次，ROI=1，收支平衡，收回成本了。
1. ROI 大于 1 就是赚了，小于 1 就是亏了。
那么，给定一个测试案例，要不要对它做自动化，判断的依据是（自动化测试）预期 ROI 至少要大于 1。
2. 自动化测试是一个长收益模式。
在理想情况下，是一次性投入（投入为开发成本），之后每运行一次，就会增加一份产出。
所以，时间越长，次数越多，收到的回报就会越大。
3. 关于开发成本（包括开发成本 d 和维护成本 m），类似估算软件开发工作量，代码行法、功能点法，我们也可以引入到估算开发工作量里，比较好掌握。
但维护成本就有点模糊了，这里包含了多种可变因素，是自动化测试项目风险的主要来源。
怎么衡量自动化的价值？什么时候开始做自动化？加特纳的技术成熟曲线，它也可以用来描述软件功能的发展过程。
新功能产生初期，一般是不稳定，经过几轮调整后，才会进入到一个平缓的阶段，这也就是稳定回归的测试阶段。
有的软件是做标准化产品的，比如专业性强的 B 端财务软件，计税模块发布出来就很稳定，我们采取的策略是在第 1 个版本做计税模块的自动化。
The Practical Organizationof Automated Software TestingAuthor: Herbert M. Isenberg Ph.D.Quality Assurance ArchitectEmail: hisen1@Key words: Automation, testing, QA, infrastructure, life cycle, reusable, automated testing, RAD.AbstractThe purpose of this paper is to take a practical approach to automated software testing and explain reqirements for its success. To be successful one needs remember that there are four interrelated components that have to work together and support one another: 1) An automated software testing system based on one point maintenance and reusable modules, 2) Testing infrastructure consisting of the events, tasks and processes that immediately support automated, as well as manual, software testing,3) Software testing life cycle that defines a set of phases outlining what testing activities to do and when to do them, and 4) Corporate support for repeatable processes. These components are discussed from the point of view of the author’s many years experience as a sen ior software test automation engineer and QA Architect working in a variety of software development environments.IntroductionThe purpose of this paper is to explain how to succeed in automated software testing. To be successful one must remember that there are several interrelated components that have to work together and support one another.This paper will lay out what these necessary components are and their interrelationships. The emphasis here is on what is practical, what is useful, and what works in the author’s experience.Automation is not an island unto itself. It requires a solid testing infrastructure and a thoughtful software testing life cycle that are both supported and valued by the corporate culture.To begin, there is the automated testing system itself. It must be designed to support reusable module and one point maintenance. It must be very flexible and easy to update.The testing infrastructure includes a dedicated test lab, a good bug tracking system, standard test case format, and comprehensive test plans.The software testing life cycle defines when tasks associated with automation occur in relation to the overall software testing effort that is mostly manual.A company can make great strides using test automation. The important benefits include, higher test coverage levels, greater reliability, shorted test cycles, ability to do multi user testing at no extra cost, all resulting in increased levels of confidence in the software.The good news is that when automated testing is introduced each of these components does not need to be in place. Pieces can be introduced gradually into the culture. What is important is the commitment to automation and an understanding of where automation can take a company.Automation success is a very practical business. The remainder of this paper is dedicated to resolving two recurrent issues confronted by those involved in automated software testing:1.How does one design and implement an effective automated software testing systemin a Rapid Application Development environment where the interfaces(windows/controls) are continually changing and the data/content is continually being revised and modified ?1.Why does automated software testing fail in most corporations?Issue 1The issue of "How to build a useful automated software testing system in a constantly change RAD environment…" , is resolved by designing the automated testing system with a set of Architecture Principles based on a Structured Methodology for building 1) Reusable modules and 2) One point system maintenance.One of the most useful outcomes of this approach is the ability of the automated testing system to change as easily and quickly as the software under test. From a corporate point of view, this is one of the key measures of success for automated testing, (and rightly so).For purposes of brevity, the development of an automated software testing design under the methodology of the architectural principles noted above, will be referred to as a Practical Automated Testing System or PATS.How does a PATS work ?This section focuses on the automated testing system itself, looks at how to build one that is simple, easy to use, supports reusable module and one point maintenance. In other words, a PATS.Tool SelectionFirst conduct a tool evaluation. Be sure to have the people who are going to use the tool do the evaluation. I suggest picking two or three tools, set them up side by side, and build a simple and complex test case in each one. Base the evaluation on ease of use and the tools ability to create reusable modules. Remember, each tool has a built in point of view on how to test. The most important point here is to not let the tool tell you how to test. The automated testing methodology is independent of the tool and the tools main role is to support that methodology.A horse will lead you to water, if you let it. That’s fine if you want to trot around the lake, however, its not so good if you want to go across the meadow. The same with automated testing tools. Out of the box, using Capture-playback, you will be taken on a ride to some wild woodland - not where you want to go. Your automated testing system will end up a big entwined mess that is impossible to maintain. This is why it is of the utmost importance that a structured testing methodology is brought to bear on the tool. The testing tool is our servant, not our master.Automated Testing Methodology:Reusable modulesThe basic building block is a reusable module. These modules are used for navigation, manipulating controls, data entry, data validation, error identification (hard or soft), and writing out logs. Reusable modules consist of commands, logic, and data. They should be grouped together in ways that make sense. Generic modules used throughout the testing system, such as initialization and setup functionality, are generally grouped together in files named to reflect their primarily function, such as "Init" or "setup". Others that are more application specific, designed to service controls on a customer window, for example, are also grouped together and named similarly.The system structure evolves by the way reusable modules are organized. Experience has shown that the most structured and practical way to organize the majority of reusable modules is around an application’s windows, or screens. All the modules that service the customer screen are organized in one file, or library. That way, when the customer screen is modified for any reason, updates to the testing system are located in one place, hence the principle of One Point Maintenance comes into existence.It may be argued that if a control, such as a list box, on the customer screen is similar to one on the inventory screen, why not use one reusable module for both ? This may be trickier to pull off than it seems, and the level of complexity may not warrant it. As a module becomesmore complex, it becomes more difficult to maintain, and there is a greater likelihood of bugs showing up (see McCabe on Cyclomatic complexity).Test casesThe next step in the methodology is to turn reusable modules into automated test cases. Here, the automation engineer takes a well-structured manual test case and begins scripting it out, creating reusable modules as he goes. The goal here is to build the reusable modules in a very methodical manner. The action-response pairs from the test case are scripted into reusable modules. These pairs also determine the size or granularity of a reusable module, which generally consist of just one action-response pair. This becomes important later, for determining the scope and cost of the automation project. A single reusable modules generally takes one to three hours to script and carefully unit test. The average automated test case requires around twenty to thirty reusable modules to complete.Automating test cases in this manner allows the testing system to take on a very predictable structure. One benefit of this predictability is the ability to begin building the automated testing system from the requirements early in the software testing life cycle.Structured Test Case FormatA structured test case format clearly represents the dynamic nature of test cases, consisting of a sequence of action-response pairs.Test case example:Test Case ID: CUST.01Function: Add a new CustomerData Assumptions: Customer database has been restoredGeneral Description:Add a new customer, via the Customer Add screen, and validate that new Customer was displayed corrected on the All Customer Screen.Benefits:1. Easier to automated - all have same structure2. Data requirements are clearly defined3. Navigation is precise4. Expected results for each action is specific - no guess work involvedPredictable StructureNext, we will examine an example of what is meant by a predictable structure. Each screen named in the test case points to a file containing a set of reusable modules. For example, if the application has thirty different screens, then the testing system will have thirty files named after those screens containing reusable modules specific to each screen. Upon executing a command intended to display a sp ecific screen, the test case’s first action is to validate that the intended screen is up, residing on the desk top, (and additionally that the screen is enabled and/or in focus - depending on the specifics of what’s being tested). This is accomplished by indexing into the file and calling the appropriate reusable module. In this case, it will be the first module in the file, which is part of the standard for validating a new screen. The reusable module knows how to 1) Dynamically validate the title of expected screen using multi level validation methods, 2) Assign an error level if the validation fails and 3) Write a message to a detailed log file. Note: I suggest at least two log files, a detail log and a one line pass/fail log.Here is an example of indexing into the customer file and calling the reusable object to validate the customer screen is displayed. In this example, the index is the label "VALIDATE":Here is an example of a reusable module that 1) Dynamically validates the customer screen using multi level validation 2) contain logic to assign error levels and 3) writes out two messages to the detail log.(code example written in 4GL language native to AutoTester toolset by Software Recording Inc.)Storing the text string value in the variable CONSTANTS.CUST_TXT, supports the principle of One Point Maintenance. This is a practical way of storing text validation strings so they are easy to locate and update.It is possible to increase or deepen the principle of One Point Maintenance, allowing for more seamless cross platform testing, or portability, through the use of generic screen and application variables. This technique allows the automated testing system to handle dynamically changing windows which is often required in a RAD environment. This increases the systems overall flexibility and reduces maintenance time.This structured method of building reusable modules continues in this same manner for each test case, resulting in a predictable, well-structured and most practical, automated testing system. Like a person laying bricks to build a house, one methodically builds reusable modules to express test case functionality. In the final analysis, this is what constitutes a Practical Automated Testing System (PATS).Multi Level ValidationMulti level data validation increases the usefulness and flexibility of the testing system. The more levels of data validation, or evaluation, the more flexible the testing system. Multi level validation and evaluation is the ability of the testing system to 1) Perform dynamic data validation at multiple levels and 2) Collect information from system messages so that an others can evaluate the data at some later point in time.Dynamic data validation is the process whereby the automated testing tool, in real time, gets data from a control, compares it to an expected value and writes the result to a log file. It also expresses the ability of the testing tool to make branching decisions based on the outcome of the comparison. Ideally, the testing tool has the ability to combine the GET and COMPARE into one command, such as a LOOK function, to simplify coding.Validation refers to correctness of data decided dynamically. Evaluation refers to system messages that will be collected but evaluated after the fact.In a PATS, dynamic data validations and message evaluations can be conducted at seven different levels with two compare modes, inclusive and exclusive.Having previously pointed out that an automated testing system is not an island unto itself, let’s continue with the section which addresses the reason wh y automated software testing most often fails.Issue 2To avoid the pitfalls commonly experienced by corporations implementing automated testing these elements must be carefully examined: 1) The testing infrastructure, 2) The software testing life cycle and 3) Corporate support.The testing infrastructureThe testing infrastructure consists of the testing activities, events, tasks and processes that immediately support automated, as well as manual, software testing. The stronger the infrastructure the more it provides for stability, continuity and reliability of the automated testing process.The testing infrastructure includes:∙Test Plan∙Test cases∙Baseline test data∙ A process to refresh or roll back baseline data∙Dedicated test environment, i.e. stable back end and front end∙Dedicated test lab∙Integration group and process∙Test case database, to track and update both automated and manual tests∙ A way to prioritize, or rank, test cases per test cycle∙Coverage analysis metrics and process∙Defect tracking database∙Risk management metrics/process (McCabe tools if possible)∙Version control system∙Configuration management process∙ A method/process/tool for tracing requirement to test cases∙Metrics to measure improvementThe testing infrastructure serves many purposes, such as:∙Having a place to run automated tests, in unattended mode, on a regular bases∙ A dedicated test environment to prevent conflicts between on-going manual and automated testing∙ A process for tracking results of test cases, both those that pass or fail∙ A way of reporting test coverage levels∙Ensuring that expected results remain consistent across test runs∙ A test lab with dedicated machines for automation enables a single automation test suite to conduct multi-user and stress testingIt is important to remember that it is not necessary to have all the infrastructure components in place in the beginning to be successful. Prioritize the list, add components gradually, over time, so the current culture has time to adapt and integrate the changes. Experience has proven it takes an entire year to integrate one major process, plus one or two minor components into the culture.Again, based on experience, start by creating a dedicated test environment and standardizing test plans and test cases. This, along with a well-structured automated testing system will go a long way toward succeeding in automation.The Software Testing Life CycleThe Software Testing Life Cycle, (STLC), is the road map to automation success. It consists of a set of phases that define what testing activities to do and when to do them. It also enables communication and synchronization between the various groups that have input to the overall testing process. In the best of worlds the STLC parallels the Software Development Life Cycle, coordinating activities, thus providing the vehicle for a close working relationship between testing and development departments.The following is a "meat-and-potatoes" list of name for the phases of the STLC:1.Planning1.Analysis1.Design1.Construction1.Testing - Initial test cycles, bug fixes and re-testing1.Final Testing and Implementation1.Post ImplementationEach phase defines five to twenty high level testing tasks or processes to prepare and execute both manual and automated testing. A few examples are in order:1.Planningo Marketing group writes a document defining the producto Define problem reporting procedureso High level test plano Begin analyzing scope of projecto Identify acceptance criteriao Setup automated test environment1.Analysiso Marketing and Development groups work together to write a productrequirements documento Develop functional validation matrices based on business requirementso Identify which test cases make sense to automateo Setup database to track components of the automated testing system, i.e.reusable moduleso Map baseline data to test cases1.Designo Development group writes a detailed document defining the architecture of the producto Revise test plan and cases based on changeso Revisit test cycle matrices and timelineso Develop risk assessment criteria (McCabe tools help here)o Formalize details of the automated testing system, i.e. file namingconventions and variableso Decide if any set of test cases to be automated lend themselves to a data driven/template modelo Begin scripting automated test cases and building reusable modulesAs the STLC is continually refined it will spell out the organization of the testing process. What steps need to be taken and when, to ensure that when the software is ready to test, both the manual and automated testing system will be in place and ready to go. The idea here is to start early and be ready to respond to change.One of the biggest reasons automation fails is the lack of preparation early in the process. This is due in part to a lack of understanding of what needs to be done and when. The steps are not difficult, it is just a matter of understanding how the STLC works. It does not take any more time and effort to succeed than it does to fail.As with the Testing Infrastructure, it is not necessary to have all the STLC tasks in place to succeed with both manual and automated testing. The most important lesson many of us have learned is "Start Early!" A great deal of automation work can be done before the software is available, if one is working with a well-structured, one point maintenance, automated testing system and methodology, i.e. PATS.The CorporationCorporation dynamics are an entire field unto themselves. The critical point regarding the corporation, for the purposes of automated software testing, is that there must be a commitment to adopting and supporting repeatable processes. Automation cannot succeed without this commitment.ConclusionPractical automated software testing systems is an evolving technology aimed at increasing longevity and reducing maintenance.Longevity reflects the automated testing systems ability to intelligently adjust and respond to unexpected changes to the application under test. By competently doing so, the testing system's life expectancy naturally increases through its inherent ability to stay useful over time.Reduced maintenance saves time. This is a function of the elimination of redundancy. exemplified by features such as "One Point" maintenance and reusable modules.PATS are designed to provide solutions to testing challenges arising from new development technologies and environments. It is important to remember that testing systems execute test cases, they do not define or prioritize them. Its takes a robust testing infrastructure and an intelligent STLC to support the practice of automated testing and establish the criteria for success.I would like to end with a couple of reminder lists:Practical features of automated software testing systems:∙Run all day and night in unattended mode∙System continues running even if a test case fails∙Keep the automated system up and running at all costs∙Recognize the difference between hard and soft errors∙Write out meaningful logs∙One point maintenance∙Easy to update reusable modules∙Text strings stored in variables easy to find and update∙Written in an English-like language easy to understand∙Automated most important business functions first.∙Quickly add scripts and modules to the system for new features∙Don’t waste time with very complex features, keep it simple∙Collect other useful information such as operating system and CASE tool message ∙Track components of the automated testing system in a database∙Track reusable modules to prevent redundancy∙Carefully test the testing system !∙Keep track of tests coverage provide by automated test suites∙Track which test cases are automated and which are manual∙Use same architecture for Web or GUI based application testing∙Make sure baseline data is defined and process in place to refresh data∙Keep test environment clean and up-to-date∙Test case management - store test cases in a database for maintenance purposes ∙Track tests that pass, as well as test that fail.Automation - How to keep it going∙Test Lab with dedicated machines∙Run test daily∙Have it become an active part of the culture∙Demonstrate the usefulness of automation to other group such as development and implementation.∙Get developers interested in the automated testing by running test for them.∙Build a suite of test specifically for developers or end users。
