The thesis investigates the possibility to enrich standard mobile phone calls with cooperative web applications. Originating from the research field know as Computer Supported Cooperative Work (CSCW) this thesis report introduces and describes the implementation of several applications which can be used by the calling parties together during a phone call. Additionally, the report describes a proof-of-concept prototype for the Android platform, and discusses the performance of cooperative web application running on mobile devices in terms of network and CPU use. The conclusions of the thesis describe a prototype application addressing and implementing the requirements as described by the theory of computer supported collaborated work. The performance of the running application showed to be satisfactory, both regarding to network demand and processor use.

This dissertation presents novel approaches to test context aware applications that suffer from a cost prohibitive number of context and GUI events and event combinations. The contributions of this work to test context aware applications under test include: (1) a real-world context events dataset from 82 Android users over a 30-day period, (2) applications of Markov models, Closed Sequential Pattern Mining (CloSPAN), Deep Neural Networks- Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), and Conditional Random Fields (CRF) applied to predict context patterns, (3) data driven test case generation techniques that insert events at the beginning of each test case in a round-robin manner, iterate through multiple context events at the beginning of each test case in a round-robin manner, and interleave real-world context event sequences and GUI events, and (4) systematically interleaving context with a combinatorial-based approach. The results of our empirical studies indicate (1) CRF outperforms other models thereby predicting context events with F1 score of about 60% for our dataset, (2) the ISFreqOne that iterates over context events at the beginning of each test case in a round-robin manner as well as interleaves real-world context event sequences and GUI events at an interval one achieves up to four times better code coverage than not including context, 0.06 times better coverage than RSContext that inserts random context events at the beginning of each test case, 0.05 times better coverage than ISContext that iterates over context events to insert at the beginning of each test case in a round-robin manner, and 0.04 times better coverage than ISFreqTwo that iterates over context events at the beginning of each test case in a round-robin manner as well as interleaves real-world context event sequences and GUI events at an interval two on an average across four subject applications and, (3) the PairwiseInterleaved technique that selects a different context event at the beginning of each test case by iterating through context covering array in a round-robin manner and systematically interleaves context with GUI events by prioritizing the execution of GUI events in new contexts achieves higher code coverage up to a factor of six when compared to Monkey, up to a factor of 1.3 when compared to a technique that generates test suites without context events, and similar code coverage when compared to ISContext that iterates over context events to insert at the beginning of each test case in a round-robin manner on an average across five subject applications.

Το περιεχόμενο της εργασίας είναι η διαδικασία ανάπτυξης της εφαρμογής "Patras' Taxi" για την πλατφόρμα συσκευών smartphone Google Android. Η εφαρμογή είναι μια ψηφιακή εκδοχή του κλασικού αναλογικού ταξιμέτρου που βρίσκεται εγκατεστημένο στα οχήματα ταξί και προορίζεται για χρήση στην πόλη της Πάτρας και την ευρύτερη περιοχή. / The content of the paper is the process of development of the "Patras' Taxi" application, for smartphone devices with Google Android. The application is a digital version of the classic analog taximeter that is installed in taxis and is destined for use in the city of Patras and the surrounding area.

<p> Browsers are used as a medium to perform various activities on the Internet and mobile applications are used on mobile devices. They let users connect to the Internet and access different services such as sending emails, watching videos, using banking services, etc. The increase in the usage of the Internet, personal computers, and mobile phones led financial institutions to democratize their services and provide omnipresent and cost-effective services to their customers, in turn attracting a large customer base. Many of these financial institu?tions store and manage sensitive user information such as account numbers and usernames, passwords, Social Security Numbers (SSNs), etc. Due to the nature of the sensitive infor?mation that these institutions manage, it makes a perfect bait for attackers to exploit and perform cyber attacks. Most of the forensic and security research observed in the bank?ing ecosystem focused on foreign financial institutions and mobile banking applications for Android. However, no forensic research has been conducted on the mobile and browser ap?plications of US financial institutions. In this research, I performed a forensic analysis on both browser and mobile applications (both Android and iOS) of US financial institutions. I conducted a forensic investigation on the JP Morgan Chase (Chase), Purdue Federal Credit Union (PFCU), Discover, and CapitalOne banks. This research found what information these banking applications store locally and where they store them to assist digital forensic investigators in investigations. </p>

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Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is that we generate user-driven event sequences through the GUI, without the source code or the model of the AUT. Hence, considerable amount of cost and time are saved by avoiding the need for model generation for generating the tests. Our results show that the systematic path exploration used by Q-learning results in higher average code coverage in comparison to the uniform random approach.

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