UI development and functionality testing automation in android application
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| বিন্যাস: | গবেষণাপত্র |
| ভাষা: | English |
| প্রকাশিত: |
Brac University
2024
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/10361/22853 |
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10361-22853 |
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Brac University |
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Institutional Repository |
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English |
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Deep learning Natural language processing CNN RNN Computer-aided software engineering User interfaces (Computer systems) Deep learning (Machine learning) |
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Deep learning Natural language processing CNN RNN Computer-aided software engineering User interfaces (Computer systems) Deep learning (Machine learning) Taus, Shehjad Ali Hasan, Riazul Rasul, Golam Tabassum, Anila Muttakin, Khondoker Al UI development and functionality testing automation in android application |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Taus, Shehjad Ali Hasan, Riazul Rasul, Golam Tabassum, Anila Muttakin, Khondoker Al |
| format |
Thesis |
| author |
Taus, Shehjad Ali Hasan, Riazul Rasul, Golam Tabassum, Anila Muttakin, Khondoker Al |
| author_sort |
Taus, Shehjad Ali |
| title |
UI development and functionality testing automation in android application |
| title_short |
UI development and functionality testing automation in android application |
| title_full |
UI development and functionality testing automation in android application |
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UI development and functionality testing automation in android application |
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UI development and functionality testing automation in android application |
| title_sort |
ui development and functionality testing automation in android application |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22853 |
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10361-228532024-05-16T21:03:14Z UI development and functionality testing automation in android application Taus, Shehjad Ali Hasan, Riazul Rasul, Golam Tabassum, Anila Muttakin, Khondoker Al Alam, Md. Golam Rabiul Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Deep learning Natural language processing CNN RNN Computer-aided software engineering User interfaces (Computer systems) Deep learning (Machine learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 43-45). 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. Shehjad Ali Taus Riazul Hasan Golam Rasul Anila Tabassum Khondoker Al Muttakin B.Sc in Computer Science 2024-05-16T09:35:26Z 2024-05-16T09:35:26Z ©2023 2023 Thesis ID: 19101539 ID: 19301168 ID: 19301126 ID: 19101157 ID: 22241176 http://hdl.handle.net/10361/22853 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 57 pages application/pdf Brac University |