DIFFERENT PRAGMATIC APPROACHES OF TESTING THE CLOUD APPLICATION USING SIMULATORS/EMULATORS

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1 DIFFERENT PRAGMATIC APPROACHES OF TESTING THE CLOUD APPLICATION USING SIMULATORS/EMULATORS Ms. Vaishali Jawale Assistant Professor ASM s Institute of Computer Studies Pimpri - Pune, Abstract: Computer simulation - (computer science) the technique of representing the real world by a computer program; "a simulation should imitate the internal processes and not merely the results of the thing being simulated". This paper addresses the following question: What can be better ways of realizing computer application simulators and emulators?. And also presents the basic concepts and idea of Simulation, software testing, and applies them to the particular Simulation software application on local machine as well as for cloud application: what must be tested, how can it be tested, and what gains can be expected when successful test strategies are applied to typical simulation software. Software testing remains a practical approach to proof software, whether it is manual or automated testing, whereas mathematical proofs are the theoretical approach. Simulation can be useful to bring software testing a step further by simulating a user, or multiple users, performing a series of tasks through the software to be tested, with or without meaningful actions, but always within a certain set of parameters. One of the testing principles is early testing - Testing should start as early as possible in the Software Development Life Cycle. So that any defects in the requirements or design phase are captured. So in this paper we are studying and comparing the effects of late testing on the software application development on local machine as well as on cloud. Keywords: Simulation, Software Testing, Cloud Computing, Automated Testing, Testing Principle. Introduction: Definition of Simulator & Emulator: A simulator is software that duplicates some processor in almost all the possible ways. An emulator is a hardware which duplicates the features and functions of a real system, so that it can behave like the actual system. Usually the emulators and simulators are used for the testing of new architectures and also to give training in some complex systems. A most famous example for a simulator is the flight simulator that simulates the functionalities of an aircraft. Basically a simulator tests the program on the development processor (the processor on which we are working), while an emulator is used on the target processor (the processor for which the program is being written). Computer Application simulators and emulators have become major tools in experimental research because of several reasons, including the higher costs of experimenting on real application. Simulation: Simulation is an effective technique to test software where testing in the real environment is not feasible. A 3D graphical simulator can make the software testing process much more effective and efficient. With graphical representation, the software's behavior can be observed in a virtual environment similar to the real environment. This kind of simulator can be best used in testing embedded system software, specifically in systems with many moving objects. 1

2 Typically, however, the creation of a simulator is expensive in terms of time and cost. In addition, it is difficult to verify the accuracy of the system. We propose an inexpensive 3D graphical simulation method for software testing of embedded systems. This system eliminates the need for mathematical modeling by modeling the physical objects with simple rules. These objects self-coordinate their operation through the application of these rules. The 3D graphical simulator integrates the entire modeling process, eliminating the need for separate simulation and graphical display units. The simulator provides instant graphical feedback of system behavior to illustrate the feasibility and reliability of the system Testing: Testing activities generally represent around 30% of the total workload for typical software projects. Testing activities generally consists in two distinct activities: - Design the tests: research the pertinent functionalities to be tested, how they can be tested and how tests can be used to detect non expected behaviours. - Pass the tests: run each identified and specified test and compare the obtained values with the expected ones. This can be time consuming as it usually requires the presence of a human to launch the tests and analyze their results. Testing activities generally occur in specific phases of a project (as shown on the figure). But it is generally well understood that some testing activities are performed during the other phases of a software development project, such as in the design phase (tests designs) and development phases (tests can be profitably passed during coding in order to detect regressions or non standard features). OVERVIEW OF TESTING TECHNIQUE Functional tests in the CAPE domain are performed on the values of the variables of a simulator, whereas non functional tests are tests that do rely on measures such as performance or quality metrics and not on the computed variables.es Behavioral tests focus on the behavior of some variables and detect that the actual behavior of such a variable is not consistent with the expected behavior of this variable. This can be expressed as bound checking for specific variables, as well as dynamic characteristics for these kinds of variables measurement of uptime, deviation from objective, etc.). Convergence tests are generally very useful on Simulation Projects because they help Process Designers to detect the reasons of the difficulty of convergence that implies specific Unit Operations. They usually imply a re-estimation of convergence criteria on the values that are tested (rate of convergence, precision) and can be run in Steady State and Dynamic simulations. Regression tests ensure that flow sheet that have been tested and validated once stay validated. It is a very important concept in Software Engineering that can be applied to CAPE software or business components in order to improve the integration of large simulators or large simulation software Regression testing consists in the process of ensuring that successive versions of the same software behave in a coherent manner. 2

3 Vendors of Simulation Environments Vendors of Simulation Environments can use regression testing to ensure that they do not introduce defects while developing new functionalities of their software. Defects that are typically introduced are generally visible by the users of the software and should be detected very quickly in order for the fixing cost of these defects to remain low. Regressions that can be detected are typically: - Crashes of the software - Warning - Numerical glitches, non physical simulation Pros: In many ways, the use of emulators and simulators is essential for developing and testing a quality application. Such technology enables developers to verify certain functionality that is not specific to any device, carrier or operating system. They are also very useful in terms of usability, and especially design, including data input, screen size, button use, etc. all from the convenience of their own laptop. Although many computer app companies rely on these tools to identify bugs at an early stage in development, they often fail to catch bugs at later stages. As we ve seen, these type of bugs are always the most expensive to correct. Cons: One of the biggest challenges for software developers is that the testing of applications is occurring in an environment far removed from the real world, where actual users run and interact with those applications ON their devices. Said differently, the gap between in-thelab simulation and in-the-wild usage is far too great to ignore. Consider, for instance, an application that is data entry intensive. To assume that it can be fully tested on a simulator and be ready for release is a mistake software app developers have continually made for the past five years. The convenience of simulators and emulators has made it easier than ever to be lured into a false sense of security. But the advantages of such tools are limited in scope, and should never be considered a substitute for real-world, ondevice testing. How exactly the simulation works: Each scenario is a compound based on a sequence of user inputs, which are collected and stored into a database. Most scenarios developed in software testing are usually based on a normal usage in order to prove the software works as intended. The existing scenarios will be subsequently mutated multiple times using probabilities, that is, the likelihood such or such user input can occur, similarly as in a Monte Carlo Simulation. The simulation system runs multiple scenarios simultaneously against multiple instances of the software during its entire development cycle on a dedicated server, on a 24/7 basis, and reports failed scenarios accordingly. The failed scenarios are then ran step-by-step by the software engineers so as to catch and correct defects. In this paper we are discussing about finding defects in an earlier stage in the development process. One of the most important activities that can be done to find these defects is to integrate evaluations (like testing) in the quality strategy of software development projects. But as all systems are becoming more and more complex, this emphasis on finding defects early is also growing. And as Boehm told us: the earlier errors are found, the cheaper it is to fix them. Advancing technologies make this possible using model. 3

4 Models can be used to generate test cases automatically (MBT), evaluate the software and can help with understanding the functionality of the software. But it s also possible to use it for other practices. In embedded software it s a common use to use model for testing the software in an early stage of the project. This way error is solved early before the software is implemented in the appliance, like a VCR, refrigerator, car or even an airplane. Within the embedded world, various hardware components are processed in the design process. OO can also do this, by using different functions as the components. These (standard) functions can be incorporated into a model and connected with each other. As a result a model of a component arises which then can be simulated. And the functional correctness of the component can be determined. Using models gives us (testers and developers) insight into the dynamics of the development process and algorithmic aspects of a system by simulation them. Therefore models can be used: as executable and testable specifications; to communicate (system) requirements and interface definitions; as a model of the entire system when provided with virtual prototypes; automatic code generation for software development (algorithm or logic), and Automatic test case generation for testing the software. Execute the functional design As a tester I find the first option particularly interesting; as executable and testable specifications. With embedded software this is maybe easier to understand. There models are used to simulate the hardware or real world, in order to test the software of the embedded system. But with OO normal software applications, such as administrative systems, can also be executed as a testable specification. When using models it is now possible to simulate the (functional) design and visualize and debug the specification. In this simulation it is possible to integrate both the initial input of the test as well as the expected result, thus creating a functional test. Within the embedded world this is called model-in-the-loop testing (MIL). Development testing A full exploration of a (functional) design based on testing scenarios by using simulations is a way to test requirements. Within these testing scenarios diverse 4

5 subjects can be included, such as input parameters and environmental factors. And therefore covering all the possible input and parameters of a component or unit and this, subsequently, leads to an increased coverage of the development tests. Best results can be achieved when the testing of the models (this is called simulation testing or virtual testing) is done parallel with the design process. When the models are created, the tests are focused on the design aspects and are simulated in the model. Since the design is still in a draft phase, you are not only able to adjust the (functional) design, but also the tests. And, as a result, a design model (at unit level) is created that enjoys the confidence of analysts, developers and testers. Conclusion: In this Paper-I explained different approaches to testing the Cloud application using Simulators/Emulators. I also did the comparison between uses of simulator on local machine and on cloud for a software application.by comparing them I concluded some points which shows that, testing the application using simulator/emulator for cloud application is having some advantages over testing the application outside the cloud on local machine. While working on these various approaches of testing I notice that, one of the testing principle, early testing - Testing should start as early as possible in the Software Development Life Cycle really affects the cost of software application testing majorly on cloud. These pose additional challenges but with a systematic, comprehensive approach to test planning, these could be handled. References: [1] Scott Tilley and Tauhida Parveen, Florida Institute of Technology. Software Testing in the Cloud Perspectives on an Emerging Discipline book. [2] Rodrigo N. Calheiros1, 3, Rajiv Ranjan2, Anton Beloglazov1, César A. F. De Rose3, and Rajkumar Buyya1. CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. [3] Neha Thakur,ISTQB certified professional, QA Lead. Performance Testing In cloud: A Pragmatic Approach. A white paper in STC [4] [5] Rajagopal Sattaluri. Testing Considerations for Application Migration to Cloud Computing, an article for cloud expo in Cloud Computing Journal. [6] [7] Srinivasan Sundara Rajan. Non-Functional Testing for Cloud, Testing for Cloud QoS,An article in Cloud Computing Journal. [8] Engineering/ 2013.pdf [9] Moving Applications to the Cloud, 2nd Edition, msdn library. [10] Linghao Zhang, Tao Xie, Nikolai Tillmann, Peli de Halleux, Xiaoxing Ma, Jian Lv. Environment Modeling for Automated Testing of Cloud Applications. In Proc of IEEE ISSN: [11] A Bitcurrent study on the performance of cloud computing platforms.june, 2010 Cloud computing performance. df 5