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5.6 未来工作
缺陷定位的目标是辅助开发人员进行代码调试. 本文中仅从定量的角度出发衡量我们提出的 BEEP 方法对于
人工调试的帮助, 即分析 BEEP 的定位结果是否能够将缺陷代码元素排在靠前位置, 但是缺少了定性分析的环节,
即考察 BEEP 的预测结果能够帮助开发人员更快更好地修复缺陷. 一种合理的实验设置是, 将 BEEP 的预测结果
展示给一部分开发人员, 而对于另一部分开发人员则不展示任何信息, 观察两组开发人员在修复相同缺陷时的表
现. 然而, 受到诸如工业界合作等因素的限制, 本文未能包含此部分内容, 故将其留作未来工作, 以便后续研究更好
地探索细粒度缺陷定位对开发人员的调试工作带来的影响.

6 总 结

缺陷定位方法旨在自动地找到与程序失效相关的代码元素, 从而在代码调试阶段为开发人员提供帮助. 本文
提出了一种基于指针网络的细粒度缺陷定位方法. 针对现有缺陷定位方法不能为开发人员提供细粒度的缺陷信息
这一问题, 本文提出利用神经网络和大数据预测与缺陷相关的代码令牌, 帮助开发人员更准确地定位缺陷代码. 本
文基于指针神经网络, 通过将缺陷程序表示为一组抽象语法树路径, 利用程序结构信息获取预测结果. 在 3 个包含
大量真实程序中补丁案例的数据集上的实验表明, 本文提出的细粒度缺陷定位方法不仅可以有效预测缺陷代码令
牌以及修复该缺陷所必需的代码变换类型, 还显著优于基于统计信息和基于机器学习的基准方法. 此外, 在细粒度
缺陷定位信息的基础上, 我们设计了基于代码补全与基于启发式的补丁生成策略从而形成完整的程序修复方案.
在多个缺陷数据集上的结果表明, 基于令牌级别缺陷定位信息的程序修复在有效性和效率两方面都具有较好前景.

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