Constructing adaptive configuration dialogs using crowd data

Abstract

As modern software systems grow in size and complexity so do their configuration possibilities. Users are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. We propose a method to construct adaptive configuration elicitation dialogs through utilizing crowd wisdom. A set of configuration preferences in the form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Process (MDP). Association rules are used to inform the model about configuration decisions that can be automatically inferred from knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. The method is applied to the privacy configuration of Facebook.

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

“…Monitoring [2,20,73,75,93], adaption [3,26,55], evolution [25,45,66,85], discovery [81], context [21,42,48]…”

“…Therefore, user's activities need to be monitored or reported to capture the usage context and users' intentions in the form of user's behaviors log. There are existing works on runtime adaptation or evolution in CrowdRE [2,15,20,21,26,45,55,81].…”

A survey of the use of crowdsourcing in software engineering

“…In order to provide customised configuration dialogs to users, Hamidi et al [185] proposed to extract configuration preferences from a crowd dataset. The optimised configuration dialogs were formed using a Markov Decision Process.…”