The efficient assignment of bug fixing tasks to software developers is of major importance in software maintenance and evolution. When those tasks are not efficiently assigned to developers, the software project might confront extra costs and delays. In this paper, we propose a strategy that minimizes the time and the cost in bug fixing by finding the best feasible developer arrangement to handle bug fixing requests. We enhance therefore a state-of-the-art solution that uses an evolutionary bi-objective algorithm by involving a scheduling-driven approach that explores more parts of the search space. Scheduling is the process of evaluating all possible orders that developers can follow to fix the bugs they have been assigned. Through an empirical study we analyze the performance of the scheduling-driven approach and compare it to state of the art solutions. A non-parametric statistical test with four quality indicator metrics is used to assure its superiority. The experiments using two case-studies (JDT and Platform) showed that the scheduling-driven approach is superior to the state of the art approach in 71% and 74% of cases, respectively. Thus, our approach offers superior performance by assigning more conveniently bug fixing tasks to developers, while still avoiding to overload developers.

将错误修复任务有效地分配给软件开发人员对于软件维护和发展至关重要。当这些任务没有有效地分配给开发人员时，软件项目可能会面临额外的成本和延迟。在本文中，我们提出了一种策略，该策略通过找到最佳可行的开发人员安排来解决错误修复请求，从而最大程度地减少错误修复的时间和成本。因此，我们通过调度驱动来增强使用演化双目标算法的最新解决方案探索搜索空间更多部分的方法。调度是评估开发人员可以用来修复已分配的错误的所有可能订单的过程。通过一项实证研究，我们分析了调度驱动方法的性能，并将其与最新解决方案进行了比较。使用具有四个质量指标指标的非参数统计检验来确保其优越性。使用两个案例研究（JDT和Platform）进行的实验表明，在71％和74％的案例中，调度驱动的方法均优于最新方法。因此，我们的方法通过为开发人员分配更方便的错误修复任务来提供卓越的性能，同时仍避免使开发人员超载。