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