Hybrid Approach for Effective Testdata Trade-Off for Software 👨‍💻️ Testing

✅ Testing is a process used to identify quality of developed 👨‍💻️ computer 💻️ software.One of the important activity ⚽️ in testing environment is automatic test case generation, independent of the way a given ⬅️🎁 software 👨‍💻️ system is designed. This project presents 🎁 a fusion approach in functional testing for generating test cases using genetic neural networks. The system is aimed to carry out the test data generation process for functional testing. The paper 📜 explains how the method can 🥫 be used to produce a set 📐 of test cases covering 📔 the most common functional existing in software 👨‍💻️ automatically. Test case inputs 🔣 are generated randomly 🔀 and ➕ the valid inputs 🔣 are selected for the proper output. The association rule mining ⛏️ techniques are used to validate the generated data sets 📐. The genetic algorithm is used to generate data values for the test cases. The testing process should be done ⌛️ in a way to scrutinize the faults still. The generated test data is validated arithmetically. This approach is applicable for systems in which large amount of inputoutput data is available.

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