Assigning bug reports to individual developers is typically a manual, time-consuming, and tedious task. In this paper, we present a framework for automated assignment of bug-fixing tasks. Our approach employs preference elicitation to learn developer predilections in fixing bugs within a given system. This approach infers knowledge about a developer's expertise by analyzing the history of bugs previously resolved by the developer. We apply a vector space model to recommend experts for resolving bugs. When a new bug report arrives, the system automatically assigns it to the appropriate developer considering his or her expertise, current workload, and preferences. We address the task allocation problem by proposing a set of heuristics that support accurate assignment of bug reports to the developers.