Pre-and post-release bug prediction in an industrial setting

错误修复仍然是创建和发展商业软件系统的最大成本之一。虽然bug修复发生在开发的所有阶段，但客户在系统发布后发现的bug比早期发现的bug要花费更多的钱来修复;此外，部署的软件中的错误会导致用户体验下降，并会影响系统和生产该系统的公司的长期声誉。此外，当这些错误发生在金融机构中时，对客户或国家的影响可能是有害的。由于这些原因，在开发过程中尽可能早地创建检测错误的策略是有利的，并且开发人员的费用和工作量最小。在这个项目中，我们提出了一个两阶段的方法来帮助金融机构中的半自动化错误检测：（1）我们预测哪些源代码单元（文件、类或方法）最有可能包含bug，以便开发人员可以更仔细地测试和审查源代码的这些部分;以及（2）当报告错误时，我们定位该错误可能被修复的单元，以便开发人员可以更快地修复该错误。

Bug fixing remains one of the biggest costs in creating and evolving commercial software systems. While bug fixing happens during all phases of development, bugs that are found by customers after the system is released cost orders of magnitude more money to fix than those caught early on; also, bugs in deployed software can lead to a diminished user experience and can affect the long-term reputation of both the system and the company that produces it. Moreover, when these bugs occur in a financial institution, the impact on either customers or the country can be detrimental. For these reasons, it is advantageous to create strategies to detect bugs as early as possible in development, and with minimal expense and effort on the part of the developers. In this project, we propose a two-phase approach to aid in semi-automated bug detection in a financial institution: (1) we predict which units of source code (file, class or method) are most likely to contain bugs so that developers can test and review those parts of the source code more carefully; and (2) when a bug is reported, we localize in which units this bug is likely to be fixed so that the developers can fix the bug sooner.