2015Unpublished venueRequires access

Comparison study of optimized test suite generation using Genetic and Memetic algorithm

Ankita A. Mundade, Tareek Pattewar

Open publisher page 6 citations

Abstract

Testing is one of the important phase of software engineering field, which checks the correctness of software. A common part in software testing is that test data are generated. For this test data, tester manually adds different test cases. But adding test cases manually is very difficult task. So we generate set of test cases called as test suite. Test case is nothing but a condition which we want to check. Code coverage is important factor in test suite. While generating a test suite, consider a inheritance tree a of class and generate test cases by considering all branches of that tree. Test case may have more than one goal, check feasibility for this particular goal. For generating optimized test suite, apply Genetic and Memetic algorithm. The aim of this test suite generation is covering all branches for maximum code coverage while keeping the minimum size. Applying both algorithms for code coverage. Code coverage of Memetic algorithm is maximum than code coverage of Genetic algorithm. We have implemented this system and for checking result use open source project such as Roops and net.

About this research paper

What this paper is about

Testing is one of the important phase of software engineering field, which checks the correctness of software. A common part in software testing is that test data are generated. For this test data, tester manually adds different test cases. But adding test cases manually is very difficult task. So we generate set of test cases called as test suite. Test case is nothing but a condition which we want to check. Code coverage is important factor in test suite. While generating a test suite, consider a inheritance tree a of class and generate test cases by considering all branches of that tree. Test case may have more than one goal, check feasibility for this particular goal. For generating optimized test suite, apply Genetic and Memetic algorithm. The aim of this test suite generation is covering all branches for maximum code coverage while keeping the minimum size. Applying both algorithms for code coverage. Code coverage of Memetic algorithm is maximum than code coverage of Genetic algorithm. We have implemented this system and for checking result use open source project such as Roops and net.

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Testing is one of the important phase of software engineering field, which checks the correctness of software. A common part in software testing is that test data are generated. For this test data, tester manually adds different test cases. But adding test cases manually is very difficult task. So we generate set of test cases called as test suite. Test case is nothing but a condition which we want to check. Code coverage is important factor in test suite. While generating a test suite, consider a inheritance tree a of class and generate test cases by considering all branches of that tree. Test case may have more than one goal, check feasibility for this particular goal. For generating optimized test suite, apply Genetic and Memetic algorithm. The aim of this test suite generation is covering all branches for maximum code coverage while keeping the minimum size. Applying both algorithms for code coverage. Code coverage of Memetic algorithm is maximum than code coverage of Genetic algorithm. We have implemented this system and for checking result use open source project such as Roops and net.

Key concepts: Test suite, Computer science, Code coverage, Model-based testing, Test case, Test Management Approach, Algorithm, Correctness

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