A Systematic Methodology for Developing Discrete Event Simulation Models of Software Develo

A Systematic Methodology for Developing Discrete Event Simulation Models of Software Development Processes
Ioana Rus, Holger Neu, and Jürgen Münch
development processes, which is derived from the authors’ collective experience from building a few simulators. We focus on describing the development process of a discrete event simulator, but this can easily be applied to different modeling approaches. For identifying and describing the modeling process, we suggest applying practices and principles from software engineering. The intended audience for this article is the modeling and simulation community of practice. This article is organized as follows: Section II presents the activities of the proposed method. For each of the activities, we will present a description and will illustrate it with examples from our own work. Section III contains a summary and conclusions, and Section IV highlights directions for future work. II. PROPOSED METHOD This method considers the development of a new simulation model without reusing or incorporating existing components. If reuse is considered (either incorporating existing components or developing for reuse), then the method has to be changed to address possible reuse of elements. We believe that the life cycle of a simulation model for long-term use is similar to the one of software, consisting of three main phases: development, deployment, and operation, including maintenance and evolution (after all, a simulation model is a software product, too). The activities within these phases can have different time order and dependencies, therefore, the resulting life cycle can take on different forms, such as waterfall, iterative, or even agile. The activities throughout the life cycle can be divided in two categories: engineering and management activities. Since most of our experience is related to the development phase of a simulator, this is what we will focus on in this article. The engineering activities for model development are: ? Requirements identification and specification for the model to be built; ? Analysis and specification of the modeled process; ? Design of the model; ? Implementation of the model; ? Verification and validation throughout development. The management activities are:
Abstract— So far there have been several efforts for developing software process simulators. However, the approaches for developing the simulators seem to have been adhoc and no systematic methodology exists. Since modeling and simulation in support of software development should become more popular (and there are signs that it does), there is a need for migrating modeling from craft to engineering. This article proposes such a systematic method, focused on the development of discrete simulation-based decision models, but extensible to other modeling approaches as well. Keywords— Software process modeling and simulation, quality assurance, process model and simulation method and engineering, discrete event modeling.
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I. INTRODUCTION
ost publications that describe simulation models for software development processes do not address the process followed for developing these models. That leads us to believe that they are developed ad-hoc, in isolation, many from scratch, and that there are no systematic and documented ways of developing them. For system dynamics models, some authors describe their approach for developing the models [4][6]. For discrete event models, no process has been published that describes the development of such a model. Reflecting on how a software process simulator has been developed and sharing this process with other model developers is very valuable, as it allows approaching simulation in a systematic manner and provides guidance and support to the modeling community. The application of such a methodology will enhance the quality of the resulting model (including its reusability and maintainability) and will reduce development effort and duration. In this article we present a method for systematically developing discrete event simulation models for software
Ioana Rus is with the Fraunhofer Center for Experimental Software Engineering, College Park, MD, 20740, USA (corresponding author's phone: 301-403-8971; fax: 301-403-8976; e-mail: irus@https://www.wendangku.net/doc/7714496003.html,). Holger Neu is with the Fraunhofer Institute Experimental Software Engineering (IESE), Sauerwiesen 6, 67661 Kaiserslautern, Germany (e-mail: Holger.Neu@iese.fraunhofer.de). Jürgen Münch is with the Fraunhofer Institute for Experimental Software Engineering (IESE) in Kaiserslautern, Germany (e-mail: muench@iese.fraunhofer.de).

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Model development planning and tracking; Measurement of the model and of the model development process; Risk management – refers to identification and tracking of risk factors, and mitigation of their effects. Some of the risk factors are: changes in customer’s requirements, changes in the description of the modeled process, non-availability of data for the quantitative part of the model.
simulation. 4) Validation of requirements The customer has to be involved in this activity and must agree with the content of the resulting model specification document. Changes can be made, but they have to be documented. Throughout this article, we will illustrate the outputs of the activities by using some excerpts from a discrete event simulator developed by us at IESE. This model and simulator supports managerial decision-making for planning the system testing phase of software development. The simulator offers the possibility of executing what-if scenarios with different values for the parameters that characterize the system testing process and the specific project. By analyzing the outputs of the simulator, its user can visualize predictions of the effects of his/her planning decisions. Example for step 1) and step 2): ? Goal: Develop a model for decision support for planning of the system testing phase, such that tradeoffs between duration, cost, and quality (remaining defects) can be analyzed and the most suitable process planning alternative can be selected, in the context of organization x. ? Questions to be answered: Q1: When to stop testing in order to reach a specified quality (number of defects expected to remain in the delivered software)? Q2: If the delivery date is fixed, what will be the quality of the product, if delivered at that time, and what will be the cost of the project? Q3: If the cost is fixed, when will the product have to be delivered and what will be its quality at that point? Q4: Should regression testing be performed? To what extent? Output parameters: O1: Cost of testing (computed from the effort) [$] for cost or [staff hours] for effort O2: Duration of testing [hours] or [days] O3: Quality of delivered software [number of defects per K line of code] (Some of the) Input parameters: I1: Numbers of requirements to be tested I2: Indicator of the “size” of each software requirement (in terms of software modules (components) that implement that requirement and their “complexity/difficulty” factor) I3: For each software module, its “complexity/difficulty” factor I4: Number of resources (test-beds and people) needed I5: Number of resources (test-beds and people) available I6: Effectiveness of test cases (historic parameter that
During the life cycle of a simulation model, different roles are involved. In the development phase, mainly the model developer and the customer are involved. We did “pair modeling” for creating the first version of the static process model and influence diagram. This seems very useful because the discussion about the model and the influences is very inspiring. A. Simulator requirements identification and specification During the requirements activity, the purpose and the usage of the model have to be defined. According to this, the questions that the model will have to answer are determined and so is the data that will be needed in order to answer these questions. Sub-activities of the requirements specification are: 1) Definition of the goals, questions, and the necessary metrics A goal-question-metrics-(GQM) [1][3] based approach for defining the goal and the needed measures seems appropriate. GQM can also be used to define and start an initial measurement program if needed. The purpose, scope and level of detail for the model is described by the goal. The questions that the model should help to answer are formulated next. Afterwards, parameters (metrics) of the model (outputs) have to be defined that (once their value is known) will answer the questions. Then the model input parameters have to be defined, which are necessary for determining the output values. The input parameters should not be considered as final after the requirements phase; during the analysis phase they will usually change. 2) Definition of usage scenarios Define scenarios (“use cases”) for using the model. For example, for answering the question: “How does the effectiveness of inspections affect the cost and schedule of the project?”, a corresponding scenario would be: “All input parameters are kept constant and the parameter inspection effectiveness is given x different values between (min, max). The simulator is executed until a certain value for the number of defects per KLOC is achieved, and the values for cost and duration are examined for each of the cases.” For traceability purpose, scenarios should be tracked to the questions they answer (for example, by using a matrix). 3) Development of test cases Test cases can be developed in the requirements phase. They help to verify and validate the model and resulting
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gives an indication about how many defects are expected to be discovered by a test case) ? Scenarios: S1: For a chosen value of the desired quality parameter, and for the fixed values of the other inputs, the simulator is run once until it stops (the desired quality is achieved) and the values of cost and duration outputs are examined. S2: The simulator is run for a simulation duration corresponding to the chosen value of the target duration parameter, and for the fixed values of the other inputs. The values of cost and quality outputs are examined. S3: For a chosen value of the desired cost parameter, and for the fixed values of the other inputs, the simulator is run once until it stops (the cost limit is reached) and the values of quality and duration are examined. S4: For a chosen value of the desired quality parameter, and for the fixed values of the other inputs, the simulator is run once until it stops (the desired quality is achieved) and the values of cost and duration outputs are examined according to the variation of the extent of regression testing. S5: The simulator is run for a simulation duration corresponding to the chosen value of the target duration parameter, and for the fixed values of the other inputs, and the values of cost and quality outputs are examined according to the variation of the extent of regression testing. S6: For a chosen value of the desired cost parameter, and for the fixed values of the other inputs, the simulator is run once until it stops (the cost limit is reached) and the values of quality and duration are examined according to the variation of the extent of regression testing.
b
a d c
Figure 1. Process analysis and specification. 1) Analysis and creation of a static process model The software process to be modeled needs to be first understood and documented. This requires that the representations (abstractions) of the process should be intuitive enough to be understood by the customer and to constitute a communication vehicle between modeler and customer. These representations lead to common definition and understanding of the object of modeling (i.e., the software process) and to a refinement of the problem to be modeled (initially formulated in the requirements specification activity). As input and sources of information for this activity, process documents are possibly supplemented with interviews with people involved in the process (or with the process "owner"). The created process model describes the artifacts used, the processes or activities performed and the roles and tools involved. The process model shows which activities transform which artifacts and how information flows through the model. In our work, to support this activity, we used Spearmint as a modeling tool [2]. 2) Creation of the influence diagram for describing the relations between parameters of the process For documenting the relationships between process parameters, we use influence diagrams. Influence factors are typically factors that change the result or behavior of other project parameters. The relations between influencing and influenced parameters are represented in an influence diagram by arrows and + or -, depending on whether variation of the factors occurs in the same way or on opposite ways. When we draw the influence diagram, we should have in mind what the inputs and outputs we identified in the requirements phase are. These inputs and, especially, the outputs have to be captured in the influence diagrams. Figure 2 presents a small excerpt of a static process model and a corresponding influence diagram.
B. Process analysis and specification The understanding, specification and analysis of the process that is to be modeled is one of the most important activities during the development of a simulation model. We divide process analysis and specification into four subactivities, as shown in Figure 1: Analysis and creation of a static process model (a), creation of the influence diagram for describing the relations between parameters of the process (b), collection and analysis of empirical data for deriving the quantitative relationships (c), and quantification of the relations (d). Figure 1 sketches the product flow of this activity, i.e., it describes which artifacts (document symbol) are used or created by each task (arrowed circle symbol).

Number of Requirments Size of Requirements
+ +
Number of Test cases + Duration for creatin Test Cases + Effort for creatin Test Cases
Test Case development Productivity People Resource +
Figure 2. Static process model (left) and influence diagram (right). The influence diagrams that we developed in this step were later refined during design and especially during implementation, driven by a better understanding of what is really needed for implementation. 3) Collection and analysis of empirical data for deriving the quantitative relationships, For identifying the quantitative relations between process parameters, we need to identify which data/metrics need to be collected and analyzed. Usually, not all required data is available, and additional metrics from the target organization should be collected. In this case, developing a process model can help to shape and focus the measurement program of a project. 4) Quantification of the relations This is the task that is probably the hardest part of the analysis. Here we have to quantify the relations and distinguish parameter types as follows: ? Calibration parameters: for calibrating the model according to the organization, like productivity values, learning, skills, and number of developers; ? Project-specific input: for representing a specific project like number of test cases, modules, and size of the tasks; ? Variable parameters: these are the parameters that are changed to analyze the results of the output variables. In general these can be the same as the calibration parameters. The scenario from the requirements or new scenarios determine the variable parameters during the model life cycle. The distinction between these parameters is often not easy and shifts, especially, for calibration and variable parameters depending on the scenarios that are addressed by the model. The mathematical quantification is done in this step. Depending on the availability of historical metric data, sophisticated data mining methods might be used to determine these relations. Otherwise, interviews with customers, experts, or literature sources have to be used. The outputs of the process analysis and specification phase are models (static model, influence diagrams, and relations) of the software development process and parameters that have to be simulated, measures (metrics) that need to be received from the real process, and a description of all the assumptions and
decisions that are made during the analysis. The latter is useful for documenting the model for later maintenance and evolution. The artifacts created during process analysis and specification have two distinct properties: the level of static or dynamic behavior they capture, and the quantitative nature of the information they represent. Figure 3 shows the values for these properties for the static process model (which is static and qualitative), the influence diagram (static and qualitative), and the end product simulator, which is quantitative and dynamic.
Static Process Model Product flow, Control flow, ...
Cause Effect Diagram Or Influence Diagramm Qualitative Quantitative
Dynamic
Discrete Event Model
Figure 3. Properties of different modeling artifacts. Throughout this activity, several verification and validation steps must be performed: ? The static model has to be a) validated against the requirements (to make sure it is within the scope, and also that it is complete for the goal stated in the requirements specs.); b) validated against the real process (here the customer should be involved). ? The parameters in the influence diagram must be checked against a) the metrics (that they are consistent) b) the input and output variables of the requirement specification. ? The relations must be checked against the influence diagrams for completeness and consistency. All the factors of the influence diagram that influence the result have to be represented, for instance, in the equation of the relation. ? The influence diagrams (possibly also the relations) must be validated by the customer with respect to their conformance to real influences. ? The units of measurement for all parameters should be checked. C. Design During this activity, the modeler develops the design of the model, which is independent of the implementation environment. The design is divided into different levels of detail, the high-level design and the detailed design.

1) High Level Design In the high level design the surrounding infrastructure is defined. Also, the basic mechanisms describing how the input and output data is managed and represented is defined, if necessary. Usually, a model's design comprises components like a database or a spreadsheet, a visualization component, and the simulation model itself together with the information flows between them. Figure 4 shows the high level design for our system testing simulation (STS) model. The model has two main modules, the Development module, which models the development of the software, and the STS Testing module, which models the system testing of the software. These two modules interact through the flow of items such as Code, Resources, and Defects. The whole model has GUI interfaces, both for input and for output. Through the input interface, the user of the simulator will provide the values of the input parameters, which will be saved in a database and then fed into the simulator. The outputs of the simulator are saved in the database and from there are then used by a visualization software and displayed in a friendly format to the user of the model.
Model
Code
different items (if more than one type of items is defined, also the combination of items). For doing the detailed design, we looked at the static process model (modeled with Spearmint) for identifying the activities, and items, and at the influence diagrams for identifying the items' attributes. The outcome of this activity is a detailed design of the model. In Figure 5 we show such a design, using a UML-like notation. The Activity object in Figure 5 models an activity in a software process. Its attributes are: duration, cost, effort, inputs, outputs, resources type and number (both people and equipment resources). The Resource object corresponds to the real world resources needed to perform an activity. An example of instances of the People_Resource sub-class of Resource would be Developer or Tester. The human resources are characterized by their productivity, represented as the attribute Productivity. The object Artifacts captures the code (SW_Artifacts) and test cases (Testing_Artifact). The code has as attributes its size, complexity, and number of defects, while the test cases have an attribute related to their capability of detecting the defects in code (Effectiveness).
Input GUI
Development module (model)
Resources Defects
STS Testing module (model)
Output GUI
I1-I8
I1-I8
O1-O3
O1-O3
Data Base
Figure 5. Example of detailed design objects. Figure 4. High level design (excerpt). The high level design is the latest point in time for deciding which type of simulation approach to use (e.g., system dynamics, discrete event, or anything else). 2) Detailed Design In the detailed design, the “low level design” of the simulation model is created. The designer has to make several decisions, such as: ? Which activities do we have to model? (what is the level of granularity for the model) ? What items should be represented? ? What are the attributes of these items that should be captured? Additionally, the designer has to define the flow of the In the design phase, verification and validation activities must be performed, for example to check consistency between high and low level design and also against process description (static, influence diagrams, and relations) and model requirements. D. Implementation During implementation, all the information and the design decisions are transferred into the simulation model. The documents from the process specification and analysis and the design are necessary as inputs. This activity in the development process depends heavily on the used simulation tool or language used and is very similar to the implementation activity for a conventional software product. Figure 6 shows an excerpt of our discrete event model developed in Extend V5.

Figure 6: Simulation model.
A. Validation and verification (model testing) Besides the validation and verification that occur throughout the development, the implemented model and simulator must be checked to see whether it is suitable for the purpose and the problems it should address. Also, the model will be checked against the reality (here the customer has to be involved). Throughout the simulator development process, verification and validation (usually implemented by reviews) must be performed after each activity. Also, traceability between different products created during the simulator’s development must be maintained, thus enabling the modeler to easily go back to correct or add elements during model development or evolution. Implementation decisions must be documented for future understanding of the current implementation and for facilitating model evolution. III. SUMMARY AND CONCLUSIONS We presented a systematic process for developing discrete event executable models. However, this process is not restricted to this modeling approach and can be applied as well to developing other types of models, such as continuous
(system dynamics) models. IV. FUTURE WORK Future work comprises the systematic integration of further activities (such as model verification and validation) into the method. Additionally, the other phases (deployment, maintenance and evolution) that are briefly sketched below, have to be refined: In the deployment phase, the model is calibrated and given to the customer. This includes several activities that have to be performed. Also, the target environment has to be analyzed to see whether something special is needed. In the calibration step, the model is calibrated with the data of the target organization. The data has to be evaluated using measurement data, expert interviews, data taken from the literature, or applying data mining techniques. During the installation step, the model is installed at the customer’s site. The installation depends on the software used to create the simulation model. If a runtime environment for the simulation model is available and all the data is contained in the model, an installation of this runtime environment can be sufficient. If the model was created using special tools and the model needs these tools for

being executed, the tools also have to be installed. In order to use the model efficiently, the user needs training, or at least advice on how to use the model. He also has to know the limitations of the model. In the maintenance and evolution phase, a model can be changed by: 1) changing values for the calibration parameters; 2) changing relations (functions, equations); 3) changing the structure of the model. These levels require different effort and modeling skills for change (increasing from 1 to 3). Possible extensions of the method could be the addition of reuse concepts and the combination of simulation and real experiments. Research questions in this area might be: How to reuse artifacts and knowledge during the simulation modeling process (e.g., reuse of influence diagrams, reuse of parts of the simulation model)? What are appropriate interfaces of a reusable model/influence diagram? What could a framework for reusing simulation models look like? How should real experiments be designed in order to support simulation modeling and to benefit from simulation? How to combine experimentation with simulation for advancing empirical studies in software engineering? We expect this proposed method to be improved by contributions from the experience of other model developers and users. ACKNOWLEDGMENT This work was supported in part by the German Federal Ministry of Education and Research (SEV Project) and the Stiftung Rheinland-Pfalz für Innovation (ProSim Project, no.: 559). We would like to thank Sonnhild Namingha from the Fraunhofer Institute Experimental Software Engineering for reviewing the first version of the article. REFERENCES [1] V. R. Basili, D. M. Weiss: A Methodology for Collecting Valid Software Engineering Data. TSE 10(6): 728-738, 1984. [2] U. Becker-Kornstaedt, F. Bella, J. Münch, H. Neu, A. Ocampo, J. Zettel, “SPEARMINT 7: User Manual“, Technical Report No. 072.02/E, Fraunhofer Institute for Experimental Software Engineering, Kaiserslautern, Germany, December 2002. [3] L. C. Briand; C. Differding; D. Rombach, “Practical Guidelines for Measurement-Based Process Improvement”. Software Process - Improvement and Practice, 2 (1996), 253-280. [4] D. Pfahl, “An Integrated Approach to Simulation-Based Learning in Support of Strategic and Project Management in Software Organisations”, Stuttgart: Fraunhofer IRB Verlag, 2001. [5] M. I. Kellner, R. J. Madachy, D. M. Raffo, “Software process simulation modeling: Why? What? How?”, Journal of Systems and Software 46, 2-3, 1999, 91-105. [6] Rus I., “Modeling the impact on cost and schedule of software quality engineering practices”, PhD Thesis, Arizona State University, Tempe, 1998.

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Unit5 Reading An Exciting Job 说课稿 Liu Baowei Part 1 My understanding of this lesson The analysis of the teaching material:This lesson is a reading passage. It plays a very important part in the English teaching of this unit. It tells us the writer’s exciting job as a volcanologist. From studying the passage, students can know the basic knowledge of volcano, and enjoy the occupation as a volcanologist. So here are my teaching goals: volcanologist 1. Ability goal: Enable the students to learn about the powerful natural force-volcano and the work as a volcanologist. 2. Learning ability goal: Help the students learn how to analyze the way the writer describes his exciting job. 3. Emotional goal: Make the Students love the nature and love their jobs. Learn how to express fear and anxiety Teaching important points: sentence structures 1. I was about to go back to sleep when suddenly my bedroom became as bright as day. 2. Having studied volcanoes now for more than twenty years, I am still amazed at their beauty as well as their potential to cause great damage. Teaching difficult points: 1. Use your own words to retell the text. 2. Discuss the natural disasters and their love to future jobs. Something about the S tudents: 1. The Students have known something about volcano but they don’t know the detailed information. 2. They are lack of vocabulary. 3. They don’t often use English to express themselves and communicate with others.

an exciting job 翻译

我的工作是世界上最伟大的工作。我跑的地方是稀罕奇特的地方，我见到的是世界各地有趣味的人们，有时在室外工作，有时在办公室里，有时工作中要用科学仪器，有时要会见当地百姓和旅游人士。但是我从不感到厌烦。虽然我的工作偶尔也有危险，但是我并不在乎，因为危险能激励我，使我感到有活力。然而，最重要的是，通过我的工作能保护人们免遭世界最大的自然威力之一，也就是火山的威胁。 我是一名火山学家，在夏威夷火山观测站（HVO）工作。我的主要任务是收集有关基拉韦厄火山的信息，这是夏威夷最活跃的火山之一。收集和评估了这些信息之后，我就帮助其他科学家一起预测下次火山熔岩将往何处流，流速是多少。我们的工作拯救了许多人的生命，因为熔岩要流经之地，老百姓都可以得到离开家园的通知。遗憾的是，我们不可能把他们的家搬离岩浆流过的地方，因此，许多房屋被熔岩淹没，或者焚烧殆尽。当滚烫沸腾的岩石从火山喷发出来并撞回地面时，它所造成的损失比想象的要小些，这是因为在岩石下落的基拉韦厄火山顶附近无人居住。而顺着山坡下流的火山熔岩造成的损失却大得多，这是因为火山岩浆所流经的地方，一切东西都被掩埋在熔岩下面了。然而火山喷发本身的确是很壮观的，我永远也忘不了我第一次看见火山喷发时的情景。那是在我到达夏威夷后的第二个星期。那天辛辛苦苦地干了一整天，我很早就上床睡觉。我在熟睡中突然感到床铺在摇晃，接着我听到一阵奇怪的声音，就好像一列火车从我的窗外行驶一样。因为我在夏威夷曾经经历过多次地震，所以对这种声音我并不在意。我刚要再睡，突然我的卧室亮如白昼。我赶紧跑出房间，来到后花园，在那儿我能远远地看见基拉韦厄火山。在山坡上，火山爆发了，红色发烫的岩浆像喷泉一样，朝天上喷射达几百米高。真是绝妙的奇景！ 就在这次火山喷发的第二天，我有幸做了一次近距离的观察。我和另外两位科学被送到山顶，在离火山爆发期间形成的火山口最靠近的地方才下车。早先从观测站出发时，就带了一些特制的安全服，于是我们穿上安全服再走近火山口。我们三个人看上去就像宇航员一样，我们都穿着白色的防护服遮住全身，戴上了头盔和特别的手套，还穿了一双大靴子。穿着这些衣服走起路来实在不容易，但我们还是缓缓往火山口的边缘走去，并且向下看到了红红的沸腾的中心。另外，两人攀下火山口，去收集供日后研究用的岩浆，我是第一次经历这样的事，所以留在山顶上观察他们

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英语选修六课文翻译第五单元 reading An exciting job I have the greatest job in the world. travel to unusual places and work alongside people from all over the world sometimes working outdoors sometimes in an office sometimes using scientific equipment and sometimes meeting local people and tourists I am never bored although my job is occasionally dangerous I don't mind because danger excites me and makes me feel alive However the most important thing about my job is that I heIp protect ordinary people from one of the most powerful forces on earth-the volcano. I was appointed as a volcanologist working for the Hawaiian Volcano Observatory (HVO) twenty years ago My job is collecting information for a database about Mount KiLauea which is one of the most active volcanoes in Hawaii Having collected and evaluated the information I help oyher scientists to predict where lava from the path of the lava can be warned to leave their houses

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新目标英语七年级下册课文Unit04 Coverstion1 A: What does your father do? B: He＇s a reporter. A:Really?That sounds interesting. Coverstion2 A:What does your mother do,Ken? B:She＇s a doctor. A:Really?I want to be a doctor. Coverstion3 A:What does your cousin do? B:You mean my cousin,Mike? A:Yeah,Mike.What does he do? B:He is as shop assistant. 2a,2b Coverstion1 A: Anna,doesyour mother work? B:Yes,she does .She has a new job. A:what does she do? B: Well ,she is as bank clerk,but she wants to be a policewoman. Coverstion2 A:Is that your father here ,Tony?

B:No,he isn't .He's working. B:But it's Saturday night.What does he do? B:He's a waiter Saturday is busy for him. A:Does he like it? B:Yes ,but he really wants to be an actor. Coverstion3 A:Susan,Is that your brother? B:Yes.it is A:What does he do? B:He's a student.He wants to be a doctor. section B , 2a,2b Jenny:So,Betty.what does yor father do? Betty: He's a policeman. Jenny:Do you want to be a policewoman? Betty:Oh,yes.Sometimes it's a little dangerous ,but it's also an exciting job.Jenny, your father is a bank clerk ,right? Jenny:Yes ,he is . Sam:Do you want to be a bank clerk,too? Jenny:No,not really.I want to be a reporter. Sam: Oh,yeah?Why? Jenny:It's very busy,but it's also fun.You meet so many interesting people.What about your father ,Sam. Sam: He's a reporter at the TV station.It's an exciting job,but it's also very difficult.He always has a lot of new things to learn.Iwant to be a reporter ,too

高中英语_An exciting job教学设计学情分析教材分析课后反思

人教版选修Unit 5 The power of nature阅读课教学设计 Learning aims Knowledge aims: Master the useful words and expressions related to the text. Ability aims: Learn about some disasters that are caused by natural forces, how people feel in dangerous situations. Emotion aims: Learn the ways in which humans protect themselves from natural disasters. Step1 Leading-in 1.Where would you like to spend your holiday? 2.What about a volcano? 3.What about doing such dangerous work as part of your job ? https://www.wendangku.net/doc/7714496003.html, every part of a volcano. Ash cloud/volcanic ash/ Crater/ Lava /Magma chamber 【设计意图】通过图片激发学生兴趣，引出本单元的话题，要求学生通过讨论, 了解火山基本信息，引出火山文化背景，为后面的阅读做铺垫。利用头脑风暴法收集学生对课文内容的预测并板书下来。文内容进行预测，培养学生预测阅读内容的能力。同时通过预测激起进一步探究 Step2. Skimming What’s the main topic of the article?

新目标英语七年级下课文原文unit1-6

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