Semantic based Text Summarization for Single Document on Android Mobile Device

The explosion of information in the World Wide Web is overwhelming readers with limitless information. Large internet articles or journals are often cumbersome to read as well as comprehend. More often than not, readers are immersed in a pool of information with limited time to assimilate all of the articles. It leads to information overload whereby readers are trying to deal with more information than they can process. Hence, there is an apparent need for an automatic text summarizer as to produce summaries quicker than humans. The text summarization research on mobile platform has been inspired by the new paradigm shift in accessing information ubiquitously at anytime and anywhere on Smartphones or smart devices. In this research, a semantic and syntactic based summarization is implemented in a text summarizer to solve the overload problem whilst providing a more coherent summary. Additionally, WordNet is used as the lexical database to semantically extract the text document which provides a more efficient and accurate algorithm than the existing summary system. The objective of the paper is to integrate WordNet into the proposed system called TextSumIt which condenses lengthy documents into shorter summarized text that gives a higher readability to Android mobile users. The experimental results are done using recall, precision and F-Score to evaluate on the summary output, in comparison with the existing automated summarizer. Human-generated summaries from Document Understanding Conference (DUC) are taken as the reference summaries for the evaluation. The evaluation of experimental results shows satisfactory results

LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities

This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation. Test script generation is a vital component of software testing, enabling efficient and reliable automation of repetitive test tasks. However, existing generation approaches often encounter limitations, such as difficulties in accurately capturing and reproducing test scripts across diverse devices, platforms, and applications. These challenges arise due to differences in screen sizes, input modalities, platform behaviors, API inconsistencies, and application architectures. Overcoming these limitations is crucial for achieving robust and comprehensive test automation. By leveraging the capabilities of LLMs, we aim to address these challenges and explore its potential as a versatile tool for test automation. We investigate how well LLMs can adapt to diverse devices and systems while accurately capturing and generating test scripts. Additionally, we evaluate its cross-platform generation capabilities by assessing its ability to handle operating system variations and platform-specific behaviors. Furthermore, we explore the application of LLMs in cross-app migration, where it generates test scripts across different applications and software environments based on existing scripts. Throughout the investigation, we analyze its adaptability to various user interfaces, app architectures, and interaction patterns, ensuring accurate script generation and compatibility. The findings of this research contribute to the understanding of LLMs' capabilities in test automation. Ultimately, this research aims to enhance software testing practices, empowering app developers to achieve higher levels of software quality and development efficiency.Comment: Accepted by the 23rd IEEE International Conference on Software Quality, Reliability, and Security (QRS 2023

Investigating Goldream Behaviour Through Dynamic Analysis

Smartphones have become more popular today and along with it Android Operating system also increasing rapidly. The Android OS is very popular because of their design where it is an open source design. So, it attracts people to use it because it is more convenient and easy. However, the openness of Android design also become it flaw because it not only attract Android user but also attacker for Android platform. Their openness design and it is easy to get their application have give advantages to attacker repackaged Android application and can upload the repackage application easily on Android market or any third party market. This brings to the increasing of android malware in the market. So, because of that reason it leads to the execution of this project where this project helps to understand how is the malware behavior and how its work especially about GoldDream malware. The method used to identify the malware behavior is by conducting a dynamic analysis technique. The behavior is being extract from the network traffic log and based on system call function. As conclusion, the behavior of GoldDream that can be identify from this research are the malware will create a database in user device which this database will log all the incoming and outgoing phone call plus with spying the incoming sms. Another behavior is it will upload the victim SIM, IMEI and IMSI information to their C&C server by embedded the information in HTTP URL