UI development and functionality testing automation in android application

Abstract

Software Automation Process involves automating the software testing process entailing machine learning models and methodologies. This may entail procedures like test case prioritization, selection and test case generation. Machine learning can be used to rate problems and recommend fixes upon that , As well as to identify software faults. Additionally, Machine Learning(ML) can be used to analyze test coverage, improve test efficiency and optimize processes. Overall, The use of machine learning in software testing automation can help to improve the speed, accuracy and efficiency of the testing process, leading to higher-quality software and a quicker time to market. Finding and correcting software bugs requires a lot of work on the part of software engineers. Traditional testing requires human search and data analysis which is not time efficient . Errors are frequently ignored because people have a tendency to make false assumptions and arrive at prejudiced conclusions. Since machine learning enables systems to learn, adapt and use the learned knowledge in the future, software testers profit from more accurate understanding. Numerous sophisticated machine learning tasks including code completion, defect prediction, bug localization, clone recognition, code search and learning API sequences can be accomplished via deep learning. Over the years, Researchers have published a variety of methods for automatically switching between programs. Ultimately, Machine learning for automated software testing is an intriguing field that has the possibility to entirely alter how software is tested. Before machine learning is widely used in software testing, There are still a few issues requiring to be solved. This paper represents, Sequence-to-sequence (Seq2Seq) modeling is a deep learning technique used in machine learning and natural language processing (NLP) for tasks involving sequences of data. It’s particularly powerful for tasks where the length of input and output sequences can vary.Again,Seq2Seq models are widely used for translating text from one language to another. The encoder processes the source language, and the decoder generates the target language. Here, We also apply encoder-decoder techniques in machine learning. Encoder-decoder techniques are fundamental in machine learning, particularly in tasks involving sequence-to-sequence modeling, natural language processing (NLP), computer vision, and more. These techniques involve two key components: an encoder and a decoder. In NLP, for example, the encoder may be a recurrent neural network (RNN) or a transformer model like BERT. In computer vision, a convolutional neural network (CNN) can serve as the encoder. These models are designed to extract relevant features from the input data. The decoder receives the context vector from the encoder and initializes its internal state. It generates an output sequence step by step, often autoregressive. For each step, it produces an element of the output sequence and updates its internal state based on previous outputs.