Pre-deployment field testing in is the process of testing software to uncover unforeseen problems before it is released in the market. It is commonly conducted by recruiting users to experiment with the software in as natural setting as possible. Information regarding the software is then sent to the developers as logs. Log data helps developers fix bugs and better understand the user behaviors so they can refine functionality to user needs. More importantly, logs contain specific problems as well as call traces that can be used by developers to trace its origins. However, developers focus their analysis on post-deployment data such as bug reports and CVS data to resolve problems, which has the disadvantage of releasing software before it can be optimized. Therefore, more techniques are needed to harness field testing data to reduce post deployment problems. We propose techniques to process log data generated by users in order to resolve problems in the application before its deployment. We introduce a metric system to predict the user perceived quality in software if it were to be released into market in its current state. We also provide visualization techniques which can identify the state of problems and patterns of problem interaction with users that provide insight into solving the problems. The visualization techniques can also be extended to determine the point of origin of a problem, to resolve it more efficiently. Additionally, we devise a method to determine the priority of reported problems. The results generated from the case studies on mobile software applications. The metric results showed a strong ability predict the number of reported bugs in the software after its release. The visualization techniques uncovered problem patterns that provided insight to developers to the relationship between problems and users themselves. Our analysis on the characteristics of problems determined the highest priority problems and their distribution among users. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2009-09-16 17:50:31.094

資訊科技的進步與智慧型手機的普及，使得人們通訊方式改變，生活也更加依賴智慧型手機。然而，電池技術卻未能支援智慧型手機長時間使用，因此手機使用者在電量管理上的行為就變得相對重要。欲研究探討手機使用者的電量管理行為模式，須建立一個包含軟、硬體及使用者的實驗平台，本研究採用經麻省理工學院驗證的Funf Framework開放性原始碼框架，作為蒐集使用者操作紀錄資料，以情境假設觀察這些資料，定義出各情境行為模式的特徵，並根據實驗數據進行所有資料驗證。根據實驗結果，大致歸納出電量管理行為模式，此結果可提供使用者使用手機在電量管理上參考，或發展智慧型電量管理應用程式，以最佳化電量管理。 / The innovation of information technology and the spread of smart phones are changing the way that people communicate and how their livings rely on smart-phones. However, the technology of battery nowadays is still insufficient to meet the need of heavy smart-phones users; therefore, it be-comes relatively important to observe and analyze the user behavior on power management. This research aims to study the patterns of user be-havior on power management by building an experimental platform with appropriate software, hardware and users. We use the Funf Open Sensing Framework, which is originally developed at the MIT Media Lab, to collect user logs on smart phones. We have observed collected data under contex-tual assumptions, identified characteristics within the context of each be-havior pattern, and validated with the experimental data. With the result of the experiment, several patterns of power management have been classified. The experimental result can be used as a reference for the users to manage battery life, or for developing applications on smart power management that best optimizes energy consumption.