Automated Test Case Output Generation Using Seq2Seq Models

The aim of this paper is to present a creative approach to generate test case outputs for a given input automatically for software testing. Sequence-to-sequence (seq2seq) model is applied. Our approach aims to address the challenge of creating meaningful test case outputs for input variations in software testing, improving efficiency and accuracy in test automation. With the help of natural language processing techniques, the model is trained on an original dataset of test inputs and their corresponding outputs, predicting the output for a given test case input. We employ evaluation metrics including BLEU, ROUGE, and JACCARD similarity scores to assess the quality of generated outputs, comparing them against reference outputs. Our initial results show that the seq2seq model has a huge potential of producing accurate test case outputs, significantly reducing manual effort in test case generation. This work demonstrates the potential for integrating Recurrent Neural Network techniques into software testing and providing a scalable solution for automated test case output generation.