Deep-TCP: An Intelligent Deep Learning Model for Automated Test Case Prioritization Systems using Neural Networks

Abstract

Test case prioritization is the process of ordering test cases based on their importance to improve regression testing efficiency. Test case prioritization approaches have been demonstrated to improve regression testing processes. But operating the whole regression test suite can be inconvenient and costly, particularly for large systems. To overcome this problem, a novel An Intelligent Deep Learning Model for Automated Test Case Prioritization Systems using Neural Networks (Deep-TCP) has been proposed. Test cases are collected from the source code repository and split into test steps. The test steps are preprocessed using Normalization, Tokenization and stemming to remove noise. After pre-processing, word embedding is computed and the embedded features are clustered using K-means clustering. The novelty of the proposed integration is where the effort is of word embedding, K-means clustering, Radial Basis Function Network (RBFN), and CNN-BiGRU to capture both semantic and sequential connections between test cases. Radial Basis Function Network (RBFN) is used for extracting the relevant features and Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) is used for testcase priority such as high priority, average priority and low priority. The proposed framework has been implemented using Python (PyTorch) and evaluated on a system with NVIDIA RTX 4090 GPU. The efficacy of the proposed Deep-TCP framework has been determined using evaluation metrics such as Average Percentage of Faults Detected (APFD). Average APFD of the proposed method is 94.5% which is higher than 71.25% at ATRL-TCP, 73.5% at QAOA-TCS and 81.5% at BootQA approaches.