Enabling Configuration-Independent Automation by Non-Expert Users

The Internet has allowed collaboration on an unprecedented scale. Wikipedia, Luis Von Ahn’s ESP game, and reCAPTCHA have proven that tasks typically performed by expensive in-house or outsourced teams can instead be delegated to the mass of Internet computer users. These success stories show the opportunity for crowd-sourcing other tasks, such as allowing computer users to help each other answer questions like “How do I make my computer do X?”. The current approach to crowd-sourcing IT tasks, however, limits users to text descriptions of task solutions, which is both ineffective and frustrating. We propose instead, to allow the mass of Internet users to help each other answer how-to computer questions by actually performing the task rather than documenting its solution. This paper presents KarDo, a system that takes as input traces of low-level user actions that perform a task on individual computers, and produces an automated solution to the task that works on a wide variety of computer configurations. Our core contributions are machine learning and static analysis algorithms that infer state and action dependencies without requiring any modifications to the operating system or applications.