Efficient elicitation of software configurations using crowd preferences and domain knowledge

摘要

As software systems grow in size and complexity, the process of configuring them to meet individual needs becomes more and more challenging. Users, especially those that are new to a system, are faced with an ever increasing number of configuration possibilities, making the task of choosing the right one more and more daunting. However, users are rarely alone in using a software system. Crowds of other users or the designers themselves can provide with examples and rules as to what constitutes a meaningful configuration. We introduce a technique for designing optimal interactive configuration elicitation dialogs, aimed at utilizing crowd and expert information to reduce the amount of manual configuration effort. A repository of existing user configurations supplies us with information about popular ways to complete an existing partial configuration. Designers augment this information with their own constraints. A Markov decision process (MDP) model is then created to encode configuration elicitation dialogs that maximize the automatic configuration decisions based on the crowd and the designers’ information. A genetic algorithm is employed to solve the MDP when problem sizes prevent use of common exact techniques. In our evaluation with various configuration models we show that the technique is feasible, saves configuration effort and scales for real problem sizes of a few hundreds of features.