The paper presents iTrace, an eye-tracking infrastructure, that enables eye tracking in development environments such as Visual Studio and Eclipse. Software developers work with software that is comprised of numerous source code files. This requires frequent switching between project artifacts during program understanding or debugging activities. Additionally, the amount of content contained within each artifact can be quite large and require scrolling or navigation of the content. Current approaches to eye tracking are meant for fixed stimuli and struggle to capture context during these activities. iTrace overcomes these limitations allowing developers to work in realistic settings during an eye tracking study. The iTrace architecture is presented along with several use cases of where it can be used by researchers. A short video demonstration is available at https://youtu.be/AmrLWgw4OEs.

A sentiment analyzer that determines the sentiment behind the typed text instantly using Naive Bayes classifier. Text pre-processing and classification for this app is done in Python and the front-end is built using React.