The RESNETS AND DBSCAN-BASED AUTOMATED TEST CASE GENERATION FOR IMPROVED PATTERN RECOGNITION IN SOFTWARE TESTING

IMPROVED PATTERN RECOGNITION IN SOFTWARE TESTING

Abstract

In this article, the author describes a new approach for automatic test case generation realized based on Residual Neural Networks (ResNet) and DBSCAN clustering as well. We successfully improve test case accuracy and reduce user engagement while not impacting testing performance by combining ResNet for pattern recognition with DBSCAN for managing noisy datasets. To develop the above-mentioned system output, we have utilized ResNet for feature extraction and used clustering using DBSCAN to get an autonomous generation of high-accuracy test cases. The proposed method is more accurate and scalable concerning the standard model-based testing; thus, it strengthens the software testing.

Background:
The complexity of software systems has been growing, requiring robust testing methodologies to provide high-quality results. Many of the classical test case generation methods require a significant amount of human intervention and may require help to handle noisy datasets effectively. In response to these issues, this study proposes a hybrid model: it first uses machine learning (ML) algorithms to predict effective guidelines from historical answers to questions, and then the predicted guidelines are incorporated into TestGeneration scripts for test case generation.

Methods:
ResNet was used for capturing patterns from input data and DBSCAN was used to cluster the patterns in a noisy environment, respectively. Therefore, this combination allowed the creation of test cases automatically with little human interaction for a more confident and reliable outcome.

Objectives:
The objectives of this study are to provide better quality in creating the test cases and handle the noisy datasets efficiently using DBSCAN for raising software testing efficiency by merging ResNet and clustering techniques along with minimizing human work during the process of testing.

Results:
Through this article, we are presenting an approach by using ResNet for feature extraction and DBSCAN clustering that helps in automatic test case generation which ensures accuracy with scalability and minimizes user interaction without hampering the testing performance.

Conclusion:
Our approach ResNet-DBSCAN is very promising and it improves automated test case generation accuracy, significantly reduces human intervention, and handles noisy datasets very well to increase overall software testing efficiency.