Software development comprises of several phases, including but not limited to requirements gathering, design, development, verification & validation, and maintenance. Software development processes are frameworks that impose structure on building a software system, using one or more of the phases above, and they are broadly classified as plan-driven and agile. Plan-driven processes (e.g. the Waterfall Model) follow a rigid structure on the order of phases from requirements gathering towards maintenance, in the order given above. They put emphasis on documentation, repeatability and stability of the phases. On the other hand, agile processes (e.g. Test Driven Development) follow an iterative and incremental approach, where the phases can be repeated while the scope of the system is expanded on each iteration. Agile processes put more emphasis on system artifacts (source code and tests) than documentation, which makes them more suitable for the work in this dissertation. Although software systems can be built following different development processes described above, maintenance is the dominating cost during the lifetime of a system, with 70%- 90% of the total cost. During maintenance, the dominating activity is program comprehension, i.e. understanding requirements and their relation with the system artifacts such as source code and tests. Therefore, obtaining as much information about requirements as possible is a major concern during maintenance. A common activity during the verification & validation phase of development is testing. It is reported that, in a typical software project, approximately 50% of the total development time is expended on testing. With the advent of agile processes, this number is even higher. During testing, test results are typically used in a binary fashion, i.e. to see if they pass or fail. However, tests contain more information about requirements that is useful to stakeholders during maintenance. In this dissertation, we develop novel techniques to understand what is captured in tests and exploit this information to provide a better understanding on the relationships between requirements, and their relationship with tests. We provide a holistic approach to using tests as a useful source of information on requirements and we develop an end-to-end automated process to benefit from the testing phase during the development and maintenance of a system

Requirements relationships express conceptual dependencies, constraints and associations among the requirements of a software system, such as dependencies and hint-relations. For stakeholders of a system, it is important and beneficial to identify requirements relationships for system design, maintenance and comprehension tasks. In this paper, we build on existing research and use features, realization of functional requirements in software, to automatically retrieve requirements relationships from existing test cases. We evaluate our approach on a chat system, Apache Pool, and Apache Commons CLI. We obtain precision/recall levels as good as or better than currently existing object-tracing and scenario-analysis based approaches when tested on the same case studies. Furthermore, our approach is resistant to scenario selection, and works for all types of systems with a profiler available, unlike existing techniques.

Pre-2018 CSE ID: CS2013-1001

Poor air quality is a growing global health concern that impacts millions of people worldwide. Although we are beginning to understand the health impacts of air pollution, it remains a challenge to provide people with the information they need to be able to make health-conscious choices. The CitiSense system gives individuals the real-time tools they need to be able to identify when and where they are exposed to poor air. We present the results of a qualitative study regarding a 4-week “in the wild” deployment of the CitiSense air-quality sensor and system. We focus on how the 16 participants responded to their new-found information about their environment, how they shared information, and what kinds of actions were enabled by having access to real-time air-quality data. Quantitative data gathered through the course of the study frames participant responses by showing what levels of pollution were experienced and what activities heightened exposure. We found that CitiSense’s real-time graphical displays and everywhere monitoring provided a critical bridge between data and experience, enabling sophisticated in-the-world sensemaking and sharing with those nearby. This in turn affected behavior and attitudes, leading to shifts in how users reasoned about their world, and how they assessed their personal choices and impact.

Pre-2018 CSE ID: CS2014-1005

In this work we present CitiSense, a new kind of "citizen infrastructure" for the monitoring of pollution and environmental conditions that users are exposed to. By utilizing mobile phones and affordable, small sensors placed in the environment and carried by users, data about pollutants such as ozone and carbon monoxide is collected and used to provide real-time feedback to users and enable them to make healthy changes in their behavior. Results can be reported to a back-end server for further processing and learning, allowing other stakeholders to better understand how diseases such as asthma develop and to help coordinate efforts within a user's community to improve conditions. What differentiates CitiSense from previous projects of this sort is the design of a complete system that addresses issues of mobile power management, data security, privacy, inference with commodity sensors, and integration into a highly extensible and adaptive infrastructure comprising of Open Rich Services (ORS). We discuss the design goals of the CitiSense project, our progress towards the vision of ubiquitous environmental sensing in San Diego, and preliminary results for energy management policies for sensor nodes and mobile phones.

Pre-2018 CSE ID: CS2011-0961