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

How Water Pollution Effects Marine Life?

For years man has been polluting our vast resource of oceans, not expecting to ever cause harm to them. Unfortunately, they were wrong. Our oceans and other waterways have become a poisonous playground of garbage, chemicals, and sewage. The effects of this ignorance has had devastating affects on the marine life and their habitat. This affects the habitat for marine life by destroying their homes. In doing so the intricate balance between marine animals and their homes can alter our oceans forever. Our very existence depends on the oceans.â€Å"Without oceans, Earth would be too hot and there would not be enough air to breathe. † (Hogan 10) The immediate importance to stop the destruction of our oceans is clear. Our oceans are not only crucial to our climate, but also provide us with food, jobs, and much loved recreation. The effects of man’s abuse can be seen on a daily basis, from the disappearance of long existing sea life such as whales, to garbage washing up on the shore, to the disastrous oil spoils that cost millions of dollars each year to clean up. In some areas the neglect is already so great that complete clean up is impossible.Pollution is the introduction of harmful contaminants that are outside the norm for a given ecosystem. Common man-made pollutants that reach the ocean include pesticides, herbicides, chemical fertilizers, detergents, oil, sewage, plastics, and other solids. Many of these pollutants collect at the ocean's depths, where they are consumed by small marine organisms and introduced into the global food chain. (oceannationalgeographic. com) The most polluted body of water on Earth is the Mediterranean Sea. Factories and ships dump over 300,000 tons of oil, toxic waste, and raw sewage in the Mediterranean each year.  (Hogan12)Even though most European countries have been working since 1975 to clean up the sea they still are living along the most polluted waters on Earth. With each year of pollutants being dumped into i t the water has darkened and the sea floor is covered with deadly slime. Plastic pollution has become a major problem throughout the world. Plastic nets, plastic garbage, and plastic medical wastes are killing millions of marine mammals, turtles, and fish. Animals may become tangled in the plastic debris or may eat it and die. Many governments have banned the dumping of plastics in oceans.  (csshome. com)There are other pollutants that affect our oceans like sediment and thermal pollution. These pollutants are mostly found in the United States. Sediment destroys spawning and feeding grounds for fish, reduces fish and shellfish populations, destroys pools used for resting, smothering eggs and fry, fills in lakes and streams, and decreases light penetration, thus endangering aquatic plants. (csshome. com) Thermal pollution refers to heating or cooling the water which changes the biota in the water. This can harm or kill organisms that rely on the water’s ecosystem.It can effe ct the way fish eggs will hatch or the fish will grow. It may even kill all living life unless they adapt to it. Scientist have counted some 400 such dead zones around the world. (ocean. nationalgeographic. com) Oil spills are another huge pollution problem that effects not only marine life but the whole ocean. Tankers spill anywhere from three to six million tons of oil int the ocean every year. It can take anywhere from two to ten years for aquatic life to recover from a spill. (csshome. com) These spills have caused severe devastation not only to marine life but the our country’s economy and livelihood of many people.Chemical pollution occurs every day all over the world. Factories are pouring deadly chemical waste into our waters. Some are dumped directly and others end up their eventually because they are poured into rivers which eventually end up in our oceans. This toxic dumping has caused severe abnormalities in our marine life and has cause almost all marine life in the White Sea off the coast of the Soviet Union to completely disappear.In Washington state some company presidents have been thrown into jail for breaking very strict pollution laws. A final type of pollution, toxic runoff, is much harder to control than pollution form factories and sewage plants. Toxic runoff is the wash off of fertilizers, pesticides and weed killers that wash off farmers fields and into our rivers and eventually into our oceans. Some problems of this type of pollution is that fertilizer runoff can cause some ocean plants to grow out of control and eventually crowd out other plants which obviously has a direct effect on the food chains of our oceans. (Hogan 20) Now that the water is so polluted people want to clean it up and change the bad affects it has had on our world’s oceans.There are many countries who are trying to clean up all their polluted waters, but one big problem is it is very costly. So they designed rules and organizations to help clean up and stop polluting. Lawmakers have made several acts to help stop pollution. The acts are the Clean Water Act, the Safe Drinking Water Act, Coastal Zone Management Act, Endangered Species Act, Rivers and Harbors Appropriation Act, and the National Environmental Policy Act. All of these acts were set up for saving animals in the water or cleaning up the water. They are now starting to fine or arrest anyone who is caught dumping in the ocean.If hindsight is 20/20 surely man would have made better choices about ocean dumping. It is apparent today that our society’s ignorance of many years has caused a lasting and extremely costly deadly effect on our earth. Together we must all work to keep the oceans clean and safe not only for the animals that live these waters but for ourselves as well. The oceans and seas of our world don’t belong to just one country or one person, but rather they are all connected making everybody responsible to protect and prohibit the continuous de struction of marine life and their habitat.

Taking Action on Earth Day

Every year, people all around the world come together to celebrate Earth Day. This annual event is marked by lots of different activities,  from parades to festivals to film festivals to running races. Earth Day events typically have one theme in common: the desire to show support for environmental issues and teach future generations about the need to protect our planet. The First Earth Day The very first Earth Day was celebrated on April 22, 1970. The event, which some consider to be the birth of the environmental movement, was founded by United States Senator Gaylord Nelson. Nelson chose the April date to coincide with spring while avoiding most spring break and final exams. He hoped to appeal to college and university students for what he planned as a day of environmental learning and activism. The Wisconsin Senator decided to create an Earth Day after witnessing the damage caused in  1969 by a massive oil spill in Santa Barbara, California. Inspired by the student anti-war movement, Nelson hoped that he could tap into the energy on school campuses to get kids to take notice of issues such as air and water pollution,  and put  environmental issues onto the national political agenda. Interestingly, Nelson had tried to put the environment on the agenda within Congress from the moment he was elected to office in 1963. But he as repeatedly told that Americans were not concerned about environmental issues. So Nelson went straight to the American people, focusing his attention on college students.   Participants from 2,000 colleges and universities, roughly 10,000 primary and secondary schools and hundreds of communities across the United States got together in their local  communities  to mark the occasion of the very first Earth Day. The event was billed as a teach-in, and event organizers focused on peaceful demonstrations that supported the environmental movement. Almost 20 million Americans filled the streets of their local communities on that first Earth Day,  demonstrating  in  support of environmental issues in rallies large and small all across the country. Events focused on  pollution, the dangers of pesticides, oil spill damage, the loss of wilderness, and the extinction of wildlife. Impacts of Earth Day The first Earth Day led to the creation of the United States Environmental Protection Agency and the passage of the  Clean Air,  Clean Water, and  Endangered Species  acts. It was a gamble, Gaylord later recalled, but it worked. Earth Day is now observed in 192 countries, and celebrated by billions of people around the world. Official Earth Day activities are coordinated by the nonprofit, Earth Day Network, which is  chaired by the first Earth Day 1970 organizer, Denis Hayes. Over the years, Earth Day has grown from localized grassroots efforts to a  sophisticated network of environmental activism. Events can be found everywhere from tree planting  activities  at your local park to  online Twitter parties that share information about environmental issues. In 2011,  28 million trees were planted in Afghanistan by the Earth Day Network as part of their Plant Trees Not Bombs campaign. In 2012, more than 100,000 people rode bikes in  Beijing to raise awareness about climate change and help people learn what they could do to protect the planet. How can you get involved? The  possibilities  are endless.  Pick up trash in your neighborhood. Go to an Earth Day festival. Make a commitment to reduce your food waste or electricity use. Organize an event in your community. Plant a tree. Plant a garden. Help to organize a community garden. Visit a national park. Talk to your friends and family about environmental issues such as climate change, pesticide use, and pollution.   The best part? You dont need to wait until April 22 to celebrate Earth Day. Make every day Earth Day and help to make this planet a healthy place for all of us to enjoy.

The Cryptic Secrets Of Egyptian Pyramids - 1719 Words

The Cryptic Secrets of Egyptian Pyramids The pyramid-shaped masonry architectures are called Pyramids, and there are eighty of them known as ancient Egyptian Pyramids. The Egyptian Pyramids are the products of a slavery country, but they are also the great accomplishments of ancient people. Since the first discovery of the Egyptian Pyramids, many scientists have been dedicated in ancient Egypt study. After years of researching on the earliest Egyptian Pyramid, Pyramid of Djoser, and the most famous Egyptian Pyramid, Pyramid of Khufu, Egyptologists have successfully solved many mysteries of Egypt Pyramid in the aspects of architecture, geography, and math. They came up with a conclusion that Egyptian Pyramids are symbols of the ruler’s power. Influenced by the major studies, nowadays, people commonly acknowledge Egypt Pyramid as the â€Å"tomb of pharaohs†. However, more questions about its purposes, building methods, and symbolic meanings have been brought up in the las t several years. The Egyptian Pyramids are not simply historical architectures, and their mysteries have not been completely solved yet. Herodotus was an ancient historian. He was born in Greece and lived in the fifth century BC. In about 450 B.C. he wrote in his historic documents that there were secret burial chambers beneath the Great Pyramid at Giza (Orcutt). After the publication of his reports, more reasons that support the idea of â€Å"tomb of pharaohs† had appeared. The Egyptologists who did ancientShow MoreRelatedAnalysis Of The Poem The Night Sky 1772 Words   |  8 Pageshis owner is safe from any threat or harm. As Courage continues to walk down across the land, he stopped by the water pump and starts digging the ground. After Courage had finished digging the ground, he found himself an eccentric yet mysterious cryptic slab being buried within the ground. The slab contains four weird markings on the slab; there was an image of a record player instrument, a wave current, a locust, and also a vague image of mummified-like person appeared on the slab. Courage took