COLLABORATIVE TESTING ACROSS SHARED SOFTWARE COMPONENTS

Large component-based systems are often built from many of the same components. As individual component-based software systems are developed, tested and maintained, these shared components are repeatedly manipulated. As a result there are often significant overlaps and synergies across and among the different test efforts of different component-based systems. However, in practice, testers of different systems rarely collaborate, taking a test-all-by-yourself approach. As a result, redundant effort is spent testing common components, and important information that could be used to improve testing quality is lost. The goal of this research is to demonstrate that, if done properly, testers of shared software components can save effort by avoiding redundant work, and can improve the test effectiveness for each component as well as for each component-based software system by using information obtained when testing across multiple components. To achieve this goal I have developed collaborative testing techniques and tools for developers and testers of component-based systems with shared components, applied the techniques to subject systems, and evaluated the cost and effectiveness of applying the techniques. The dissertation research is organized in three parts. First, I investigated current testing practices for component-based software systems to find the testing overlap and synergy we conjectured exists. Second, I designed and implemented infrastructure and related tools to facilitate communication and data sharing between testers. Third, I designed two testing processes to implement different collaborative testing algorithms and applied them to large actively developed software systems. This dissertation has shown the benefits of collaborative testing across component developers who share their components. With collaborative testing, researchers can design algorithms and tools to support collaboration processes, achieve better efficiency in testing configurations, and discover inter-component compatibility faults within a minimal time window after they are introduced.

A HUMAN-CENTERED APPROACH TO IMPROVING THE USER EXPERIENCE OF SOFTWARE UPDATES

Software updates are critical to the security of software systems and devices. Yet users often do not install them in a timely manner, leaving their devices open to security exploits. This research explored a re-design of automatic software updates on desktop and mobile devices to improve the uptake of updates through three studies. First using interviews, we studied users’ updating patterns and behaviors on desktop machines in a formative study. Second, we distilled these findings into the design of a low-fi prototype for desktops, and evaluated its efficacy for automating updates by means of a think-aloud study. Third, we investigated individual differences in update automation on Android devices using a large scale survey, and interviews. In this thesis, I present the findings of all three studies and provide evidence for how automatic updates can be better appropriated to fit users on both desktops and mobile devices. Additionally, I provide user interface design suggestions for software updates and outline recommendations for future work to improve the user experience of software updates.

User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.