EFFECTIVE APPROACH FOR DYNAMIC TEST CASE GENERATION FOR LOAD TESTING OF HTTP WEB SERVER

Transcription

1 EFFECTIVE APPROACH FOR DYNAMIC TEST CASE GENERATION FOR LOAD TESTING OF HTTP WEB SERVER Shweta Ahuja M.Tech. Research Scholar Computer Science and Engineering Guru Nanak Institute of Technology Mullana, Haryana, India Shalini Kapoor Assistant Professor Computer Science and Engineering Guru Nanak Institute of Technology Mullana, Haryana, India ABSTRACT Test case generation is one of the prominent domain and key component in the software testing. The test cases are utmost important as the performance of module or entire system can be evaluated with unbiased test cases. A number of approaches are developed so far for the test case generation still this area is under research because of multiple services and applications which are processed by the software. In this research work, a new approach name Monte Carlo Simulation based test case generation is used. Using this methodology, a unique and any number of test cases can be generated and then processed for the actual performance evaluation of the web server.

2 Keywords Software Testing, Test Case Generation, Monte Carlo Based Test Case Generation INTRODUCTION Test Case Generation is one of the critical errands in programming building that is having a situated of conditions under which a tester will figure out if an application, programming framework or one of its components is functioning as it was initially settled for it to do. The instrument for figuring out if a product project or framework has breezed through or fizzled such a test is known as a test prophet. In a few settings, a prophet could be a necessity or utilization case, while in others it could be a heuristic. This process take numerous test cases to verify that a product project or framework is considered adequately investigated to be discharged. Test cases are regularly alluded to as test scripts, especially when composed - when they are normally gathered into test suites. Monte Carlo systems (or Monte Carlo examinations) are an expansive class of computational calculations that depend on rehashed irregular inspecting to get numerical results. They are regularly utilized as a part of physical and scientific issues and are most helpful when it is troublesome or difficult to utilize other numerical techniques. Monte Carlo systems are primarily utilized as a part of three particular issue classes: streamlining, numerical joining, and creating draws from a likelihood conveyance. In the proposed work, we will create a grouping of test cases to assess the execution and uprightness of web application so that the simultaneous and high load applications can be executed with no overhead. Programming testing is an examination led to furnish partners with data about the nature of the item or administration under test. Programming testing can likewise give a target, free perspective of the product to permit the business to acknowledge and comprehend the dangers of programming execution. Test methods incorporate the procedure of executing a

3 system or application with the purpose of discovering programming bugs (lapses or different imperfections). PROBLEM FORMULATION AND PROPOSED WORK A program is evaluated based on number of test cases. The program is having multiple elements in terms of modules, functions, variables, constants and many others. A new version of program P1 can be generated by the modifications in base program P. Now, the base work attempts to find out the similarity of Test Cases of P1 and P. Using this base approach, the work emphasize on the execution of a function that performs the fault detection in the program. The work focus on the prioritization of test cases by which the faults can be reduced in the program. The proposed work is using Monte Carlo Method for generation of dynamic random files which are processed as test cases. Each test case (randomly generated file) is processed by the HTTP Apache Web Server and the process time is evaluated in terms of microseconds. The work focus on finding the Upper Bound Lower Bound and Mean Limit by which the number of files (test cases) and the file size (weights of the test cases) can be fixed. By using the proposed approach of setting bounds, the front-end can be set so that the limited traffic/files can be entered and processed. As the domain of Software Testing and Test Case Generation is much diversified, there is lots of scope of research for the scholars and practitioners. The main objectives of this research are to develop and deploy a web based simulator to generate the sequence of test cases. These test cases shall have different priorities based on fuzzy random values. The effect and overall performance of the web based software application shall be investigated based on the priority and fuzzy random values. The test cases shall be generated using monte carlo simulation that is one of the prominent method to generate the inputs and expected outcome. In base paper, there is no measurement of

4 execution time and overhead issues. to calculate the execution time and complexity of the existing algorithm in the base paper. This work propose a new algorithm that will be better than in the base work using greedy method in terms of enhanced test case generation. SIMULATION SCENARIOS AND RESULTS For implementation and simulation, a web based simulator is developed. Using the simulator, any number of test cases can be generated and tested on the HTTP Web Server. Tools Technologies Used for Simulation PHP MySQL Database Engine AJAX JQuery JPGraph Library Dia - The Diagram Editor Figure 1 Simulation Scenario

5 Figure Generation of Dynamic Test Cases using Simulator Figure 3 Evaluation of the Current Test Cases in terms of File Size and Execution Time

6 Number of files Average File Average execution time Size (In Bytes)

7 CORRELATION ANALYSIS -> NUMBER OF FILES VS. EXECUTION TIME

8 X Values Y Values X - M x Y - M y (X - M x ) (Y - M y ) (X - M x )(Y - M y ) Result Details & Calculation X Values = 355 Mean = 7.45 (X - M x ) = SS x = Y Values = 3.54 Mean = (Y - M y ) = SS y = X and Y Combined N = 49 (X - M x )(Y - M y ) =

9 R Calculation r = ((X - M y )(Y - M x )) / ((SS x )(SS y )) r = / (( )(38.306)) = Meta Numerics (cross-check) r = Key X: X Values Y: Y Values M x : Mean of X Values M y : Mean of Y Values X - M x & Y - M y : Deviation scores (X - M x ) & (Y - M y ) : Deviation Squared (X - M x )(Y - M y ): Product of Deviation Scores The value of R is This is a strong positive correlation, which means that high X variable scores go with high Y variable scores (and vice versa). The value of R, the coefficient of determination, is CORRELATION ANALYSIS -> AVERAGE FILE SIZE VS. EXECUTION TIME X Values Y Values X - M x Y - M y (X - M x ) (Y - M y ) (X - M x )(Y - M y )

10 Result Details & Calculation X Values = Mean = (X - M x ) = SS x = Y Values = 3.54 Mean = (Y - M y ) = SS y = X and Y Combined N = 49 (X - M x )(Y - M y ) =

11 R Calculation r = ((X - M y )(Y - M x )) / ((SS x )(SS y )) r = / (( )(38.306)) = 0.03 Meta Numerics (cross-check) r = 0.03 Key X: X Values Y: Y Values M x : Mean of X Values M y : Mean of Y Values X - M x & Y - M y : Deviation scores (X - M x ) & (Y - M y ) : Deviation Squared (X - M x )(Y - M y ): Product of Deviation Scores The value of R is Although technically a positive correlation, the relationship between your variables is weak (nb. the nearer the value is to zero, the weaker the relationship). The value of R, the coefficient of determination, is Following is the results of simulation in terms of files in different slots of sizes. Number of files Average File Average execution time Size (In Bytes)

12 Figure 4 Consistency Analysis of Web Server in terms of Execution Time From the above graph, the performance of web server is almost consistent in terms of execution time. If number of files are, the average execution time of the test cases are increasing in Linear Distribution. By this aspect, the performance of server is moderate and acceptable because the web server is not behaving in abnormal way and the linear growth of output is followed. Following is the results of simulation in terms of files in different slots of average files size.

13 Number of files Average File Average execution time Size (In Bytes) Figure 5 Performance Evaluation of Web Server in terms of Execution Time with Number of Simulation Attempts Here it is evident that performance of web server is steady in most of the cases. The server is not behaving and processing the results in abnormal way. Following is the results of simulation in terms of variations in files size. Number of files Average File Size Average execution time

14 (In Bytes)

15 Figure 6 Performance Evaluation with File Size and Execution Time Now, it is clear the results are abnormal attempt 0 where the number of files is 48 and average file size is 89. In this case, the execution time is 5.97 ms which is very high as compared to the other values. It is evident from the results that the web server is having huge

16 overhead in case of increasing number of files rather than single file with more size. The web server is able to handle less number of files in more effective way as compared to more files with lesser size. CONCLUSION This research work is focused on the dynamic generation of test cases so that that performance of web server can be analyzed. In this research work, a new approach using Monte Carlo Simulation is used for the generation of dynamic files of different size so that the biasing can be avoided and actual load test of the server can be implemented. It is found that that the web server is performing in consistent approach in terms of different parameters including execution time, cost and performance. REFERENCES [1] Beizer, B. (003). Software testing techniques. Dreamtech Press. [] Weyuker, E. J. (1998). Testing component-based software: A cautionary tale.ieee software, (5), [3] El Far, I. K., & Whittaker, J. A. (001). Model Based Software Testing.Encyclopedia of Software Engineering. [4] Kaner, C., Bach, J., & Pettichord, B. (008). Lessons learned in software testing. John Wiley & Sons. [5] Memon, A. M., Pollack, M. E., & Soffa, M. L. (001). Hierarchical GUI test case generation using automated planning. Software Engineering, IEEE Transactions on, 7(), [6] Tung, Y. W., & Aldiwan, W. S. (000). Automating test case generation for the new generation mission software system. In Aerospace Conference Proceedings, 000 IEEE (Vol. 1, pp ). IEEE. [7] Bai, X., Dong, W., Tsai, W. T., & Chen, Y. (005, October). WSDL-based automatic test case generation for web services testing. In Service-Oriented System Engineering, 005. SOSE 005. IEEE International Workshop (pp. 07-1). IEEE.

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