Metrics and Models in Software Testing

Metrics and Models in Software Testing How do we measure the progress of testing? When do we release the software? Why do we devote more time and resources for testing a particular module? What is the reliability of software at the time of release? Who is responsible for the selection of a poor test suite? How many faults do we expect during testing? How much time and resources are required to test a software? How do we know the effectiveness of test suite? We may keep on framing such questions without much effort? However, finding answers to such questions are not easy and may require significant amount of effort. Software testing metrics may help us to measure and quantify many things which may find some answers to such important questions. 10.1 Software Metrics â€Å"What cannot be measured, cannot be controlled† is a reality in this world. If we want to control something we should first be able to measure it. Therefore, everything should be measurable. If a thing is not measurable, we should make an effort to make it measurable. The area of measurement is very important in every field and we have mature and establish metrics to quantify various things. However, in software engineering this â€Å"area of measurement† is still in its developing stage and may require significant effort to make it mature, scientific and effective. 10.1.1 Measure, Measurement and Metrics These terms are often used interchangeably. However, we should understand the difference amongst these terms. Pressman explained this clearly as [PRES05]: â€Å"A measure provides a quantitative indication of the extent, amount, dimension, capacity or size of some attributes of a product or process. Measurement is the act of determining a measure. The metric is a quantitative measure of the degree to which a product or process possesses a given attribute†. For example, a measure is the number of failures experienced during testing. Measurement is the way of recording such failures. A software metric may be average number of failures experienced per hour during testing. Fenton [FENT04] has defined measurement as: â€Å"It is the process by which numbers or symbols are assigned to attributes of entities in the real world in such a way as to describe them according to clearly defined rules†. The basic issue is that we want to measure every attribute of an entity. We should have established metrics to do so. However, we are in the process of developing metrics for many attributes of various entities used in software engineering. Software metrics can be defined as [GOOD93]: â€Å"The continuous application of measurement based techniques to the software development process and its products to supply meaningful and timely management information, together with the use of those techniques to improve that process and its products.† Many things are covered in this definition. Software metrics are related to measures which, in turn, involve numbers for quantification, these numbers are used to produce better product and improve its related process. We may like to measure quality attributes such as testability, complexity, reliability, maintainability, efficiency, portability, enhanceability, usability etc for a software. We may also like to measure size, effort, development time and resources for a software. 10.1.2 Applications Software metrics are applicable in all phases of software development life cycle. In software requirements and analysis phase, where output is the SRS document, we may have to estimate the cost, manpower requirement and development time for the software. The customer may like to know cost of the software and development time before signing the contract. As we all know, the SRS document acts as a contract between customer and developer. The readability and effectiveness of SRS document may help to increase the confidence level of the customer and may provide better foundations for designing the product. Some metrics are available for cost and size estimation like COCOMO, Putnam resource allocation model, function point estimation model etc. Some metrics are also available for the SRS document like number of mistakes found during verification, change request frequency, readability etc. In the design phase, we may like to measure stability of a design, coupling amongst modules, cohesion of a module etc. We may also like to measure the amount of data input to a software, processed by the software and also produced by the software. A count of the amount of data input to, processed in, and output from software is called a data structure metric. Many such metrics are available like number of variables, number of operators, number of operands, number of live variables, variable spans, module weakness etc. Some information flow metrics are also popular like FANIN, FAN OUT etc. Use cases may also be used to design metrics like counting actors, counting use cases, counting number of links etc. Some metrics may also be designed for various applications of websites like number of static web pages, number of dynamic web pages, number of internal page links, word count, number of static and dynamic content objects, time taken to search a web page and retrieve the desired information, similarity of web pages etc. Software metrics have number of applications during implementation phase and after the completion of such a phase. Halstead software size measures are applicable after coding like token count, program length, program volume, program level, difficulty, estimation of time and effort, language level etc. Some complexity measures are also popular like cyclomatic complexity, knot count, feature count etc. Software metrics have found good number of applications during testing. One area is the reliability estimation where popular models are Musas basic executio n time model and Logarithmic Poisson execution time model. Jelinski Moranda model [JELI72] is also used for the calculation of reliability. Source code coverage metrics are available that calculate the percentage of source code covered during testing. Test suite effectiveness may also be measured. Number of failures experienced per unit of time, number of paths, number of independent paths, number of du paths, percentage of statement coverage, percentage of branch condition covered are also useful software metrics. Maintenance phase may have many metrics like number of faults reported per year, number of requests for changes per year, percentage of source code modified per year, percentage of obsolete source code per year etc. We may find number of applications of software metrics in every phase of software development life cycle. They provide meaningful and timely information which may help us to take corrective actions as and when required. Effective implementation of metrics may improve the quality of software and may help us to deliver the software in time and within budget. 10.2 Categories of Metrics There are two broad categories of software metrics namely product metrics and process metrics. Product metrics describe the characteristics of the product such as size, complexity, design features, performance, efficiency, reliability, portability, etc. Process metrics describe the effectiveness and quality of the processes that produce the software product. Examples are effort required in the process, time to produce the product, effectiveness of defect removal during development, number of defects found during testing, maturity of the process [AGGA08]. 10.2.1 Product metrics for testing These metrics provide information about the testing status of a software product. The data for such metrics are also generated during testing and may help us to know the quality of the product. Some of the basic metrics are given as: (i) Number of failures experienced in a time interval (ii) Time interval between failures (iii) Cumulative failures experienced upto a specified time (iv) Time of failure (v) Estimated time for testing (vi) Actual testing time With these basic metrics, we may find some additional metrics as given below: (i) (ii) Average time interval between failures (iii) Maximum and minimum failures experienced in any time interval (iv) Average number of failures experienced in time intervals (v) Time remaining to complete the testing. We may design similar metrics to find the indications about the quality of the product. 10.2.2 Process metrics for testing These metrics are developed to monitor the progress of testing, status of design and development of test cases and outcome of test cases after execution. Some of the basic process metrics are given below: (i) Number of test cases designed (ii) Number of test cases executed (iii) Number of test cases passed (iv) Number of test cases failed (v) Test case execution time (vi) Total execution time (vii) Time spent for the development of a test case (viii) Total time spent for the development of all test cases On the basis of above direct measures, we may design following additional metrics which may convert the base metric data into more useful information. (i) % of test cases executed (ii) % of test cases passed (iii) % of test cases failed (iv) Total actual execution time / total estimated execution time (v) Average execution time of a test case These metrics, although simple, may help us to know the progress of testing and may provide meaningful information to the testers and project manager. An effective test plan may force us to capture data and convert it into useful metrics for process and product both. This document also guides the organization for future projects and may also suggest changes in the existing processes in order to produce a good quality maintainable software product. 10.3 Object Oriented Metrics used in Testing Object oriented metrics capture many attributes of a software and some of them are relevant in testing. Measuring structural design attributes of a software system, such as coupling, cohesion or complexity, is a promising approach towards early quality assessments. There are several metrics available in the literature to capture the quality of design and source code. 10.3.1 Coupling Metrics Coupling relations increase complexity, reduce encapsulation, potential reuse, and limit understanding and maintainability. The coupling metrics requires information about attribute usage and method invocations of other classes. These metrics are given in table 10.1. Higher values of coupling metrics indicate that a class under test will require more number of stubs during testing. In addition, each interface will require to be tested thoroughly. Metric Definition Source Coupling between Objects. (CBO) CBO for a class is count of the number of other classes to which it is coupled. [CHID94] Data Abstraction Coupling (DAC) Data Abstraction is a technique of creating new data types suited for an application to be programmed. DAC = number of ADTs defined in a class. [LI93] Message Passing Coupling. (MPC) It counts the number of send statements defined in a class. Response for a Class (RFC) It is defined as set of methods that can be potentially executed in response to a message received by an object of that class. It is given by RFC=|RS|, where RS, the response set of the class, is given by [CHID94] Information flow-based coupling (ICP) The number of methods invoked in a class, weighted by the number of parameters of the methods invoked. [LEE95] Information flow-based inheritance coupling. (IHICP) Same as ICP, but only counts methods invocations of ancestors of classes. Information flow-based non-inheritance coupling (NIHICP) Same as ICP, but only counts methods invocations of classes not related through inheritance. Fan-in Count of modules (classes) that call a given class, plus the number of global data elements. [BINK98] Fan-out Count of modules (classes) called by a given module plus the number of global data elements altered by the module (class). [BINK98] Table 10.1: Coupling Metrics 10.3.3 Inheritance Metrics Inheritance metrics requires information about ancestors and descendants of a class. They also collect information about methods overridden, inherited and added (i.e. neither inherited nor overrided). These metrics are summarized in table 10.3. If a class has more number of children (or sub classes), more amount of testing may be required in testing the methods of that class. More is the depth of inheritance tree, more complex is the design as more number of methods and classes are involved. Thus, we may test all the inherited methods of a class and testing effort well increase accordingly. Metric Definition Sources Number of Children (NOC) The NOC is the number of immediate subclasses of a class in a hierarchy. [CHID94] Depth of Inheritance Tree (DIT) The depth of a class within the inheritance hierarchy is the maximum number of steps from the class node to the root of the tree and is measured by the number of ancestor classes. Number of Parents (NOP) The number of classes that a class directly inherits from (i.e. multiple inheritance). [LORE94] Number of Descendants (NOD) The number of subclasses (both direct and indirectly inherited) of a class. Number of Ancestors (NOA) The number of superclasses (both direct and indirectly inherited) of a class. [TEGA92] Number of Methods Overridden (NMO) When a method in a subclass has the same name and type signature as in its superclass, then the method in the superclass is said to be overridden by the method in the subclass. [LORE94] Number of Methods Inherited (NMI) The number of methods that a class inherits from its super (ancestor) class. Number of Methods Added (NMA) The number of new methods added in a class (neither inherited, nor overriding). Table 10.3: Inheritance Metrics 10.3.4 Size Metrics Size metrics indicate the length of a class in terms of lines of source code and methods used in the class. These metrics are given in table 10.4. If a class has more number of methods with greater complexity, then more number of test cases will be required to test that class. When a class with more number of methods with greater complexity is inherited, it will require more rigorous testing. Similarly, a class with more number of public methods will require thorough testing of public methods as they may be used by other classes. Metric Definition Sources Number of Attributes per Class (NA) It counts the total number of attributes defined in a class. Number of Methods per Class (NM) It counts number of methods defined in a class. Weighted Methods per Class (WMC) The WMC is a count of sum of complexities of all methods in a class. Consider a class K1, with methods M1,†¦Ã¢â‚¬ ¦.. Mn that are defined in the class. Let C1,†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.Cn be the complexity of the methods. [CHID94] Number of public methods (PM) It counts number of public methods defined in a class. Number of non-public methods (NPM) It counts number of private methods defined in a class. Lines Of Code (LOC) It counts the lines in the source code. Table 10.4: Size Metrics 10.4 What should we measure during testing? We should measure every thing (if possible) which we want to control and which may help us to find answers to the questions given in the beginning of this chapter. Test metrics may help us to measure the current performance of any project. The collected data may become historical data for future projects. This data is very important because in the absence of historical data, all estimates are just the guesses. Hence, it is essential to record the key information about the current projects. Test metrics may become an important indicator of the effectiveness and efficiency of a software testing process and may also identify risky areas that may need more testing. 10.4.1 Time We may measure many things during testing with respect to time and some of them are given as: 1) Time required to run a test case. 2) Total time required to run a test suite. 3) Time available for testing 4) Time interval between failures 5) Cumulative failures experienced upto a given time 6) Time of failure 7) Failures experienced in a time interval A test case requires some time for its execution. A measurement of this time may help to estimate the total time required to execute a test suite. This is the simplest metric and may estimate the testing effort. We may calculate the time available for testing at any point in time during testing, if we know the total allotted time for testing. Generally unit of time is seconds, minutes or hours, per test case. Total testing time may be defined in terms of hours. Time needed to execute a planned test suite may also be defined in terms of hours. When we test a software, we experience failures. These failures may be recorded in different ways like time of failure, time interval between failures, cumulative failures experienced upto given time and failures experienced in a time interval. Consider the table 10.5 and table 10.6 where time based failure specification and failure based failure specification are given: Sr. No. of failure occurrences Failure time measured in minutes Failure intervals in minutes 1 12 12 2 26 14 3 35 09 4 38 03 5 50 12 6 70 20 7 106 36 8 125 19 9 155 30 10 200 45 Table 10.5: Time based failure specification Time in minutes Cumulative failures Failures in interval of 20 minutes 20 01 01 40 04 03 60 05 01 80 06 01 100 06 00 120 07 01 140 08 01 160 09 01 180 09 00 200 10 01 Table 10.6: Failure based failure specification These two tables give us the idea about failure pattern and may help us to define the following: 1) Time taken to experience ‘n failures 2) Number of failures in a particular time interval 3) Total number of failures experienced after a specified time 4) Maximum / minimum number of failures experienced in any regular time interval. 10.4.2 Quality of source code We may know the quality of the delivered source code after reasonable time of release using the following formula: Where WDB: Number of weighted defects found before release WDA: Number of weighted defects found after release The weight for each defect is defined on the basis of defect severity and removal cost. A severity is assigned to each defect by testers based on how important or serious is the defect. A lower value of this metric indicates the less number of error detection or less serious error detection. We may also calculate the number of defects per execution test case. This may also be used as an indicator of source code quality as the source code progressed through the series of test activities [STEP03]. 10.4.3 Source Code Coverage We may like to execute every statement of a program at least once before its release to the customer. Hence, percentage of source code coverage may be calculated as: The higher value of this metric given confidence about the effectiveness of a test suite. We should write additional test cases to cover the uncovered portions of the source code. 10.4.4 Test Case Defect Density This metric may help us to know the efficiency and effectiveness of our test cases. Where Failed test case: A test case that when executed, produced an undesired output. Passed test case: A test case that when executed, produced a desired output Higher value of this metric indicates that the test cases are effective and efficient because they are able to detect more number of defects. 10.4.5 Review Efficiency Review efficiency is a metric that gives insight on the quality of review process carried out during verification. Higher the value of this metric, better is the review efficiency. 10.5 Software Quality Attributes Prediction Models Software quality is dependent on many attributes like reliability, maintainability, fault proneness, testability, complexity, etc. Number of models are available for the prediction of one or more such attributes of quality. These models are especially beneficial for large-scale systems, where testing experts need to focus their attention and resources to problem areas in the system under development. 10.5.1 Reliability Models Many reliability models for software are available where emphasis is on failures rather than faults. We experience failures during execution of any program. A fault in the program may lead to failure(s) depending upon the input(s) given to a program with the purpose of executing it. Hence, time of failure and time between failures may help us to find reliability of software. As we all know, software reliability is the probability of failure free operation of software in a given time under specified conditions. Generally, we consider the calendar time. We may like to know the probability that a given software will not fail in one month time or one week time and so on. However, most of the available models are based on execution time. The execution time is the time for which the computer actually executes the program. Reliability models based on execution time normally give better results than those based on calendar time. In many cases, we have a mapping table that converts execution time to calendar time for the purpose of reliability studies. In order to differentiate both the timings, execution time is represented byand calendar time by t. Most of the reliability models are applicable at system testing level. Whenever software fails, we note the time of failure and also try to locate and correct the fault that caused the failure. During system testing, software may not fail at regular intervals and may also not follow a particular pattern. The variation in time between successive failures may be described in terms of following functions: ÃŽ ¼ () : average number of failures upto time ÃŽ » () : average number of failures per unit time at time and is known as failure intensity function. It is expected that the reliability of a program increases due to fault detection and correction over time and hence the failure intensity decreases accordingly. (i) Basic Execution Time Model This is one of the popular model of software reliability assessment and was developed by J.D. MUSA [MUSA79] in 1979. As the name indicates, it is based on execution time (). The basic assumption is that failures may occur according to a non-homogeneous poisson process (NHPP) during testing. Many examples may be given for real world events where poisson processes are used. Few examples are given as: * Number of users using a website in a given period of time. * Number of persons requesting for railway tickets in a given period of time * Number of e-mails expected in a given period of time. The failures during testing represents a non-homogeneous process, and failure intensity decreases as a function of time. J.D. Musa assumed that the decrease in failure intensity as a function of the number of failures observed, is constant and is given as: Where : Initial failure intensity at the start of testing. : Total number of failures experienced upto infinite time : Number of failures experienced upto a given point in time. Musa [MUSA79] has also given the relationship between failure intensity (ÃŽ ») and the mean failures experienced (ÃŽ ¼) and is given in 10.1. If we take the first derivative of equation given above, we get the slope of the failure intensity as given below The negative sign shows that there is a negative slope indicating a decrementing trend in failure intensity. This model also assumes a uniform failure pattern meaning thereby equal probability of failures due to various faults. The relationship between execution time () and mean failures experienced (ÃŽ ¼) is given in 10.2 The derivation of the relationship of 10.2 may be obtained as: The failure intensity as a function of time is given in 10.3. This relationship is useful for calculating present failure intensity at any given value of execution time. We may find this relationship Two additional equations are given to calculate additional failures required to be experienced to reach a failure intensity objective (ÃŽ »F) and additional time required to reach the objective. These equations are given as: Where à ¢Ã‹â€ Ã¢â‚¬  ÃŽ ¼: Expected number of additional failures to be experienced to reach failure intensity objective. : Additional time required to reach the failure intensity objective. : Present failure intensity : Failure intensity objective. and are very interesting metrics to know the additional time and additional failures required to achieve a failure intensity objective. Example 10.1: A program will experience 100 failures in infinite time. It has now experienced 50 failures. The initial failure intensity is 10 failures/hour. Use the basic execution time model for the following: (i) Find the present failure intensity. (ii) Calculate the decrement of failure intensity per failure. (iii) Determine the failure experienced and failure intensity after 10 and 50 hours of execution. (iv) Find the additional failures and additional execution time needed to reach the failure intensity objective of 2 failures/hour. Solution: (a) Present failure intensity can be calculated using the following equation: (b) Decrement of failure intensity per failure can be calculated using the following: (c) Failures experienced and failure intensity after 10 and 50 hours of execution can be calculated as: (i) After 10 hours of execution (ii) After 50 hours of execution (d) and with failure intensity objective of 2 failures/hour (ii) Logarithmic Poisson Execution time model With a slight modification in the failure intensity function, Musa presented logarithmic poisson execution time model. The failure intensity function is given as: Where ÃŽ ¸: Failure intensity decay parameter which represents the relative change of failure intensity per failure experienced. The slope of failure intensity is given as: The expected number of failures for this model is always infinite at infinite time. The relation for mean failures experienced is given as: The expression for failure intensity with respect to time is given as: The relationship for additional number of failures and additional execution time are given as: When execution time is more, the logarithmic poisson model may give large values of failure intensity than the basic model. Example 10.2: The initial failure intensity of a program is 10 failures/hour. The program has experienced 50 failures. The failure intensity decay parameter is 0.01/failure. Use the logarithmic poisson execution time model for the following: (a) Find present failure intensity. (b) Calculate the decrement of failure intensity per failure. (c) Determine the failure experienced and failure intensity after 10 and 50 hours of execution. (d) Find the additional failures and additional and failure execution time needed to reach the failure intensity objective of 2 failures/hour. Solution: (a) Present failure intensity can be calculated as: = 50 failures = 50 failures = 0.01/falures Hence = 6.06 failures/hour (b) Decrement of failure intensity per failure can be calculated as: (c) Failure experienced and failure intensity after 10 and 50 hours of execution can be calculated as: (i) After 10 hours of execution (ii) After 50 hours of execution (d) and with failure intensity objective of 2 failures/hour (iii) The Jelinski Moranda Model The Jelinski Moranda model [JELI72] is the earliest and simples software reliability model. It proposed a failure intensity function in the form of Where = Constant of proportionality N = total number of errors present i = number of errors found by time interval ti. This model assumes that all failures have the same failure rate. It means that failure rate is a step function and there will be an improvement in reliability after fixing an error. Hence, every failure contributes equally to the overall reliability. Here, failure intensity is directly proportional to the number of errors remaining in a software. Once we know the value of failure intensity function using any reliability model, we may calculate reliability using the equation given below: Where ÃŽ » is the failure intensity and t is the operating time. Lower the failure intensity and higher is the reliability and vice versa. Example 10.3: A program may experience 200 failures in infinite time of testing. It has experienced 100 failures. Use Jelinski-Moranda model to calculate failure intensity after the experience of 150 failures? Solution: Total expected number of failures (N) = 200 Failures experienced (i) =100 Constant of proportionality () = 0.02 We know = 2.02 failures/hour After 150 failures = 0.02 (200-150+1) =1.02 failures/hour Failure intensity will decrease with every additional failure experience. 10.5.2 An example of fault prediction model in practice It is clear that software metrics can be used to capture the quality of object oriented design and code. These metrics provide ways to evaluate the quality of software and their use in earlier phases of software development can help organizations in assessing a large software development quickly, at a low cost. To achieve help for planning and executing testing by focusing resources on the fault prone parts of the design and code, the model used to predict faulty classes should be used. The fault prediction model can also be used to identify classes that are prone to have severe faults. One can use this model with respect to high severity of faults to focus the testing on those parts of the system that are likely to cause serious failures. In this section, we describe models used to find relationship between object oriented metrics and fault proneness, and how such models can be of great help in planning and executing testing activities [MALH09, SING10]. In order to perform the analysis we used public domain KC1 NASA data set [NASA04] The data set is available on www.mdp.ivv.nasa.gov. The 145

Physics of a Mountain Bike Essay example -- Physics Papers

Physics of a Mountain Bike This paper will discuss some of the physics involved in bicycles. Specifically, mountain bikes with suspension. A bicycle is a very energy efficient vehicle. Its wheels allow its rider to make full use of inertia and gravitational potential energy. The basic design of a bicycle has been around for over 100 years and compared to other things, has changed very little. Some of the topics that will be covered are: frames, wheels, suspension forks, suspension frames, brakes and how all these things affect speed, momentum and stability. Some of the key parts of any bicycle are the frame, forks and wheels. The most common type of bicycle frame is known as the double diamond. It is called this because if you look at the frame, it is diamond shaped with 2 smaller triangles forming the front and rear. This designed has endured because it is easy to build, strong and efficient. The tubing that a frame is made with is usually round but can be many different shapes even on the same bike. The reason for this shaping is to increase the rigidity or improve the ride quality. For instance, the sloping tube that goes from the front of the bike to the pedal junction of the frame is sometimes ovalized along the horizontal axis at the pedal junction to make the frame flex less side to side. Frames have been made from many materials including wood, steel, aluminum, titanium and carbon fiber just to name a few. Each of the materials have a strength to weight ratio, tensile strength and cost of production that must be taken into accou nt before deciding which will work best. For example, when a frame made of beryllium was introduced, the strength to weight ratio was very high. The weight was extremely low but the cost wa... ...etty. The faster you go, the smoother it gets and the greater the force required to stop. Mass times velocity is not fun when applied to a crash. Mike Farrentino in an article in Bike magazine says, â€Å"The faster you go, the longer the crash, the greater the force of the impact. Seconds seem like hours, and the clatter and gasp and thud of cart wheeling bike and body parts is not a sound easily forgotten. †¦In spite of this, whatever any of us say, we all want to go fast†¦.Momentum is the secret key to the universe. It is the rush of joy. It is the heavy hand of gravity. It is the grin of a child, feet off the pedals, bombing downhill on a tricycle.† Sources Bike magazine: volume 10, number 2 March 2003 Bloomfield, Louis A : How things work, the physics of everyday life, 1997 Kirkpatrick & Wheeler: Physics, a world view, third edition, 1998 www.howstuffwork.com

A Visual Analysis of a World War II Poster Essay

â€Å"Keep this Horror from Your Home,† an American propaganda poster produced during World War II, uses many visual and textual elements to encourage Americans to buy war bonds by portraying the Japanese as rapists. The author of the piece is unknown, but it is obvious that the creator of the poster carefully chooses the color, patterns, scale, text, and representation of the piece to send a very strong social and political message. There is little doubt that the underlying message was directed towards all Americans during World War II, and not towards a single community or group of people. The viewer does not need an art background or even the slightest political awareness to understand the meaning of the poster. Rather, the poster is simple and straightforward. The central focus of the poster is on the Japanese man, whose facial features are clearly exaggerated to instill fear and disgust in the eyes of the viewer. In contrast, the frightened and vulnerable expression of the American woman, who is helpless in the grasp of the Japanese soldier, fuels the viewer’s hatred for the Japanese even more. Its message is palpable to the extent of the quickest glance, yet there is more evidence hidden beyond the surface of the poster. Its full visual potential can be realized only after analyzing the various visual and textual elements in exacting detail. After examining and exploring the poster in depth, it becomes apparent that the color scheme plays an important role in assigning the role of the victim and of the enemy. The Japanese soldier has very dark skin whereas the American girl being threatened and possibly raped has fair skin. The dark versus light contrast always seems to symbolize the struggle between evil and good. The technique in which the creator of the poster uses in juxtaposing the dark skin of the Japanese soldier with the light skin of the American girl fully achieves his intended purpose. Further inspection reveals another dark versus light contrast. The Japanese soldier wears a black army issued hat while the American soldier on the lower right corner of the poster wears a white cap, subconsciously suggesting that the Japanese army is evil and that the American forces are good. The blood red color of the background, the bright red lips of the Japanese soldier, and the reddish hue on the American girl’s cheeks all seem to allude to the sexuality of the poster. The use of the color red symbolizes blood, violence, and rape.  Besides the red background and the dark versus light contrast of the Japanese soldier and the American girl’s skin colors, the â€Å"placards† that read â€Å"Invest 10% in War Bonds† and â€Å"Back up Our Battleskies!† are the only other colors represented on the entire poster. It is interesting to note that the color of the two placards matches the color of the American girl’s skin. The entire poster is dominated, if not limited, to these three colors: red, dark, and light. This allows the viewer to focus on and to look beyond the color scheme and recognize its symbolic meaning, rather than just to admire the colors of the poster without delving beneath the surface meaning. Scale and pattern also play an important role in defining our perception of each character in the poster. For example, the Japanese soldier’s hands are so large that they appear to envelop the American girl’s face, causing the viewer to feel threatened. In addition, the Japanese soldier’s teeth are jagged and massive, immediately drawing attention to his beast-like, merciless expression. One may even draw the conclusion that the Japanese soldier represents some kind of wild animal, as his posture and expression seem too cruel to be human. His frightening and imposing figure in conjunction with the phrase, â€Å"Keep This HORROR from Your Home† gives the reader the feeling that both his own country and home are being threatened by the Japanese. At this point, the text and the font size and style of the text become very important to the overall meaning of the poster and make it a very effective propaganda poster. In assigning the word â€Å"HORROR† a larger font size than the words around it, the artist wants the viewer to focus on the word â€Å"Horror,† which instills a sense of fear and insecurity into the viewer. Italicizing the word â€Å"Your† gives the viewer a sense of identity with his country and inspires a sense of pride and obligation in defending his own home, his own nation. That’s where the genius of the poster comes in. Following the feeling of anxiety and urgency that the poster pushes onto the viewer, a simple solution is offered: â€Å"Invest 10% in War Bonds.† The artist obviously designed the poster to stimulate the sales of war bonds, and he succeeds in doing so because he presents such a morbid and exaggerated scene, climaxing with the threatening phrase â€Å"Keep This HORROR from Your Home.† In a literal sense, the phrase, â€Å"Invest 10% in War Bonds,† is analogous to the denouement of the poster’s â€Å"plot. † After analyzing the poster in full detail, one understands all the aspects of the poster. Questions as to why the artist used certain color schemes, why he presented the characters like he did, and why he used such powerful text are answered. Like most typical World War II propaganda posters, this poster, capitalizes on the nationalistic pride of the people in defending their own nation. In doing so, it exploits the stereotypical view of the Japanese soldier and paints a cloud of sin to rouse the spirits of Americans. Racial inferiority, hate crimes, and discrimination are the end results of such propaganda posters. Does the artist carry out his intended goal of selling war bonds and stirring up emotions of anger and fear in Americans? Without a doubt†¦ But whether such use of propaganda is ethical and justified is an entirely separate question.

How to answer Where do you see yourself in 5 yearsÂÂ

How to answer Where do you see yourself  in 5 years   There’s a question that seems to pop up all the time, and you can count on hearing it from inquiring bosses, curious colleagues, friends and family members, and perhaps most often- and most importantly- on job interviews. It’s ‘Where do you see yourself in five years?† How you answer this can say plenty about you. Want to learn how to best handle this question if you’re ever confronted with it and there’s a lot depending on your answer, like possibly getting a new job? Typically, this question arises on interviews in an effort to gauge your long-term career aspirations, as well as your ability to be self-reflective and think quickly on your feet. If you were faced with this question, would you be able to competently answer it in a way that would reflect positively on you? You certainly should be, because your answer may reveal a lot about you to the hiring personnel who are evaluating you as a potential candidate.If you’re starting to ge t anxious about whether or not your ability to answer this question effectively passes muster, don’t be- there are several strategies you can take advantage of in order to handle it well and improve your position during interviews. The Balance published an article  that covers some helpful strategies for letting the world know where you see yourself in 5 years. Make the most of the advice presented here to knock this question out of the park when it comes your way.Be honestThe first thing to do when confronted with this question is to take a deep breath and relax. Why? Because it’s not a trick question and it’s not designed to be. You shouldn’t feel pressured into making up a fantastical answer that demonstrates a desire to be on some unrealistic career trajectory. At its core, of your answer should reflect an honest but carefully considered and logical look at how you’d like to see your professional goals expand and evolve over time. It’s ok to reflect a level of ambition; in fact, your answer should show a desire for growth- no one fully respects a stagnant mindset. Just make sure that your answer shows a deep understanding of the industry you’re pursuing, reflects a level of growth that someone can reasonably hope to achieve in 5 years (going from the mailroom to the CEO’s desk may not be the best response, even if it’s what you’re aiming for), and includes a plan for making it all happen if pressed to elaborate.Don’t hop over the position in front of youYes, you want your answer to this question to highlight the fact that your ambitious and hope to achieve professional growth over the next 5 years, but you don’t want your answer to make it seem as if the current job in front of you is merely a brief stepping stone that you hope to leapfrog past quickly. Make sure your answer doesn’t come across that way.Mind your body languageAlways be sure that your delivery is ju st as strong as the content of your answer, because your audience will certainly be paying attention. Be sure to answer confidently and succinctly with impeccable body language and focus, and you’ll be doing you and your well-thought-out answer a real service.Highlight your giftsWhenever there’s a good opportunity to highlight your skill set and abilities on a job interview you should take it- it really doesn’t hurt to remind an interviewer what makes you a great candidate who potentially stands out from the crowd of applicants. So make sure that you back up your 5-year plan with reasons how and why your talents will help make it all possible.A few good answersUptowork published an article that highlights several well-crafted answers to the question â€Å"Where do you see yourself in 5 years?† Consider adapting these sample responses to fit your situation and goals.â€Å"In five years, I want to complete the internal training program for my position. Iâ €™ve read about it on your website, and I think it’s a fabulous program. Not only would I get all the training for my role, but I would be on the fast track to becoming a project manager. That’s my top career goal. Plus, my ideal path would include working abroad for a couple of years. I understand that it’s of value to you to find people prepared to do so.†Ã¢â‚¬Å"One of the reasons I want to work for PG is because I find your personalized approach to training attractive. I’m excited about the opportunity to work with a mentor and immerse myself in learning new skills. I’m also the type of employee that likes to hit the ground running and jump into projects as soon as possible. So, over the next five years, I see myself taking on as many complex assignments as the position would allow. By the end of that period, I want to say that I’ve built lasting client relationships. I want to say that I’m one of the best Salespeople on the team. I wouldn’t mind becoming someone who could train and mentor others when the time comes as well.†Ã¢â‚¬Å"As a marketing professional, I want to develop my skill set. At the end of the next five years, I want to know how to use software like Photoshop or InDesign. I want to have a better understanding of social media and video marketing. Plus I’d like to get into project management. I would like to learn on the job. Regardless, I want to look into online or evening courses. My hope is that I can apply my new skills to my job with you.†There’s no need to get nervous at the thought of having to tackle the (almost inevitable) question â€Å"Where do you see yourself in 5 years?† on interviews and other high-stakes situations. Use the strategies and advice presented here to put your best foot forward when answering this classic question. Good luck!Source: [zety.com]

The Perfect Hand Essay

The Perfect Hand Essay The Perfect Hand Essay Perfect Hand Scaled Webbed Hand In the not so distance future, global warming will melt the ice caps around the world and thus elevate the water level that would cause it to flood most of the land that we know today. So most of the living beings that live among the land would either have to evolve to be able to fly or swim to survive. Thus most of the creatures living on earth would be more aquatic. Humans might evolve and grow webbed hands and feet covered with scales to become faster in water to catch prey without losing the ability to roam the remaining land that hasn't flooded yet. In the cell biology factor, it will develope a special type of enzymes and protein that turn the outer layer of the hand and the rest of the skin blue, thus making it camouflage in the water and easier to catch prey and avoid predators. The for microbiology, the new hand we be able to produce a new type of cells that will create scales that will be light and be very tough to break. It will have similar characteristics to carbon fiber. It will also be able to help cut through the water for improvement in agility and speed in the water. For the immunology factor, the hand will develop antibodies that are resistant to both aquatic and land bacteria, viruses, and foreign substances than are possibly pathogenic. The blood inside the hand and in the muscles will be able to hold large amount of oxygen that enables the organism to swim down into the water for long periods of time. In the Plant Biology section, it will grow small green spots to gain some vitamin D from the sun to develop better and strong muscles for rapid swimming, since most of its body will be covered in scales. It will also be able to produce spores that might paralyze its prey. For the growth of its developmental state, it will have claws for fingernails and have razor like

Current Macroeconomic Situations Essay Example | Topics and Well Written Essays - 500 words

Current Macroeconomic Situations - Essay Example In the first quarter of this year the US economy has grown only by 1.8% whereas in the previous quarter it grew by 3.1% (Bangalore, 2011). As far as unemployment rate is concerned, it was at 9.1% in the month of August, 2011. There is hardly any improvement when compared to the data of previous months. According to a report that is recently published by the Bureau of Labor Statistics, at present, almost 14 million people are unemployed in the country (Bureau of Labor Statistics, 2011). What is more important is that in August the US economy could not create any ‘net new’ jobs. In July almost 85000 jobs were created but it is much lower than the expected figure 1,17,000 (BBC, 2011). It is found that Afro-Americans are mainly unemployed (16.7%) followed by the Hispanics (11.3%). There are many experts who believe that such a situation is actually the signal of the fact that US economy is preparing to get into another recession in the near future. In August it was found tha t the probability of recession in US was one fourth. A more dangerous report came in when Wall Street Journal published the result of a survey in September. According to this study, economists now believe that the chance of US economy sliding into another recession within next one year is now stands at 1-in-3 (Zappe, 2011). Inflation, in US is not a big concern. However, in 2010 inflation rate was 1.6% which is quite high as compared to previous year’s figure (-0.3%) (Central Intelligence Agency, n.d.). In August, 2011 inflation rate was found to be at 3.8%. The consumer price index has increased by 0.4% in that month (Trading Economics, n.d.). The current economic situation in US can be viewed as the failure of fiscal and monitory policies that are adopted by the government. Considering the facts like GDP growth is affected, jobs are not getting created and consumer confidence on US economy

Art History Essay Example | Topics and Well Written Essays - 1000 words - 1

Art History - Essay Example Gauguin’s ia orana Maria (Hail Mary) of 1891 and Osamu Tezuka’s Astro Boy character derived from his popular manga series in Japan which was first released in the year 1952 but has been followed by a series of renditions over the years as the character grew more popular (Gordon 52). These two pieces of art can be said to be as different as they can get from one another and portray the various changes that have occurred in the world of art over the years. P. Gauguin, Ia orana Maria (Hail Mary), 1891. Astro Boy by Osamu Tezuka Cultural Context The cultural context of P. Gauguin’s painting is quite clearly based on the Christian religion as the name itself suggests and is based on the holy mother Mary and her son Jesus in a Tahitian setting. The angel on the left of the painting revealing these two characters to two onlookers (two women) further depicts the religious inspiration that was used to bring this piece of work into being. The garb won by the characters in the painting (that is, pareus which are made of cotton with printed design and is draped around the waist) also exhibits the traditional nature of the painting. The background shows no modern influence and focuses clearly on an inspiration from past events. The Astro boy art work on the other hand is very different from the first painting and depicts a largely transformed society from the one that individuals are used to where robotic boys flying around the place is the order of the day. The piece of art which was done years after P. Gauguin’s tries to display how the artist sees the future will be like. The background content depicting various other planets shows how high the main character (Astro boy) has flown thus in a way displaying the level of technology that will be in place at that time. Stylistic characteristics The stylistic characteristics that are used art works can also be used to define the category of the art work as well (Gordon 68). The first point of note t hat can be made is the material that was used in the creation of the art works. In P. Gauguin†s Ia orana Maria (Hail Mary), oil paintings and a canvas were the basic materials used while the creation of Astro boy’s image relied mainly on computer graphics and other modern technology. This can be seen in the various characteristics of both art works such as the lines, in the Ia orana Maria there is a smoothness and sense of curvature in the lines used to paint the characters while the lines in Astro Boys depiction are sharper and clearly structured giving it a more defined looked. The same goes for the colors in both illustrations as Ia orana Maria’s colors have a warmer tone as compared to Astro boy whose colors have more depth and are clearly defined from one another unlike the older painting which exudes a sense of blending in. The shapes are more defined in the Astro boy illustration as well thanks to the clearer lines used in the drawing. Subject matter The s ubject matter is another topic where these two pieces of art greatly defer from one another. Whereas in P. Gauguin’s Ia orana Maria the subject is based on religion shown by the depiction of the son of God and his holy mother, the Astro boy art is mainly based on the future and progressing technology shown by the depiction of a flying robotic boy. The older painting can be said to display a sense of the author’s beliefs while the second more recent art work can be said to di