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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|7408||2012||10 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Procedia Engineering, Volume 30, 2012, Pages 191–200
Software test suite optimization is one of the most important problems in software engineering research. This paper deals with Automatic Generation of Feasible Independent Paths and Software Test Suite Optimization using Artificial Bee Colony (ABC) based novel search technique. In this approach, ABC combines both global search methods done by scout bees and local search method done by employed bees and onlooker bees. The parallel behavior of these three bees makes generation of feasible independent paths and software test suite optimization faster. Test Cases are generated using Test Path Sequence Comparison Method as the fitness value objective function. This paper also presents an approach for the automated generation of feasible independent test path based on the priority of all edge coverage criteria. Finally, this paper compares the efficiency of ABC based approach with various approaches.
Software engineering process is to achieve a high quality, high reliable software and always follows a software development life cycle process. One of the major activity in every software development life cycle is the software testing. Software testing process requires much effort with a human interface. So, this paper mainly gives foundation for generating automated testing by the automated generation of independent test paths and test suite optimization. Software testing  mainly includes two majormethods i.e., is black box testing and white box testing. White box testing (or structural testing) is to test thoroughly the internals of the particular program module. Test data generation is an un-decidable problem and can be non-deterministic (NP-hard) [3, 4] or a solution exists which is not practical. The highly non-linear structure of software presents a challenge to search algorithms for finding optimal and efficient test data from a complex, discontinuous, nonlinear input search space. The basic approach for evaluating input value sets in dynamic structural test data generation methods  can be summarized as -- (1) Represent a set of input values as an initial solution, (2) Apply these input values to the code and observe the generated path, and (3) Compare the generated path with the desired path and calculate fitness values. This paper presents an ABC based search algorithm to generate test data. In this research work, the functionality of the bee is extended to do the testing and monitoring activity so that it reduces the manual work and improves the confidence on the software by testing it with the coverage of the given software.Bee colony consists of three types of bees, namely scout bees, which randomly searches for the food sources, onlookerbee decides which food sources to be explored next from the list food sources given by scout bees, and lastly employee bees will search for new food source in neighborhood of exhausted food source[6, 7].Further explanation about bee colony is given in proposed strategy. This paper includes back ground work in section (II), Proposed strategy for generation of independent test paths and test suite optimization in section (III), Analysis of proposed approach in section (IV), Case study in section(V), Comparison with existing works like ACO, Genetic algorithm and Tabu search in section (VI), Conclusion and future work in section (VII).
نتیجه گیری انگلیسی
In this paper, ABC technique is used for the generation of the test data. The parallel behavior of the bees makes generation of test cases faster and efficient. Here, independent test path coverage criterion is used as objective criteria to achieve the all test coverage with less number of test runs. The proposed parallel ABC based approach solves the common local optima problem. Path sequence comparison performs better than many fitness functions proposed earlier. We propose to improvise path coverage criterion and automating search for optimized feasible independent test paths by reducing number of iterations. This algorithm can be extended to generate other test data types like strings.