A Process Web Application Testing Using TAO Tool Search Based Genetic Algorithm

Authors

  • N Sudheer Professor CSE, SIETK, Puttur, India
  • V Sharma Professor CSE, Aligarh Muslim University,Aligarh, India
  • S Hrushikesava Raju Associate Professor CSE, SIETK, Puttur, India

Keywords:

Search-based Software Engineering, Evolutionary Algorithms, Optimization Problem, Evolutionary Testing, Heuristic Search Techniques, Web applications, World Wide Web, Web testing, Survey, Performance

Abstract

Search-based Software Engineering is use number of software engineering models. In domain Search-Based Software Engineering many application is test data generation. We propose many methods for automating results bottleneck finding using search-based input-sensitive application profiling. Our key idea is to use a genetic algorithm as a search heuristic for obtaining combinations of input functions values that maximizes number of function to represents the elapsed execution time of the application. We present TAO tool is a software testing tool result automated test and oracle generation based on a semantic model. TAO is worked grammar-based test generation with automated semantics evaluation using a denotation semantics framework. The quality of web application is a broad review of recent Web testing advances model and discuss their goals, targets, techniques employed, inputs/outputs and stopping criteria. This research paper presents result testing of web application using reactive-based framework for reducing the cost and increasing efficiency of the performance testing. Finally test case can be generated automatically by solving and modify the problem using evolutionary algorithm. This model is attractive because it take a suite of adaptive automated and semi-automated solutions in situations many large complex problem spaces with multiple competing and conflicting objectives.

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Published

2025-11-11

How to Cite

[1]
N. Sudheer, V. Sharma, and S. Hrushikesava Raju, “A Process Web Application Testing Using TAO Tool Search Based Genetic Algorithm”, Int. J. Comp. Sci. Eng., vol. 4, no. 7, pp. 94–100, Nov. 2025.

Issue

Vol. 4 No. 7 (2016): IJCSE July Edition

Section

Research Article

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