I am interested in the extensive empirical analysis of the software development process and technique. My research efforts involve, developing and empirically validating effective methods and tools for improving the quality of software artifacts. It uses approaches that have been applied successfully in other domains and adapt them to the task of improving software quality. A key focus of my research is to understand the thought process of software engineers to escalate the quality of the software product. My multidisciplinary research is dedicated to improving and estimating software quality by utilizing approaches from Cognitive Psychology (CP), Behavioral Science (BS), and biological science to address a serious problem in Software Engineering.  Specifically, my dissertation research borrowed learning and eye-tracking patterns from Psychology research, and Capture-Recapture (a probabilistic defect estimation approach) from biologists to bear on the task of improving software inspection performance. The current research utilizes the reading patterns of inspectors to select the most effective inspectors for detecting different fault types. The Ensemble-based learning method was able to accurately predict effective inspectors for the inspection task.