CAREER: Analysis and Automation of Systematic Software Modifications

软件系统不断发展。开发人员必须添加特性、修复bug并重写软件系统，以提供更好的功能和更高的性能。现有系统还需要迁移到新的硬件、计算环境、编程模型和库。在我们今天发展软件系统的方式中存在冗余、低效和易出错的问题。特别是，最近的实证研究表明，开发人员经常对类似的上下文应用类似但不完全相同的更改。进行这种系统的、重复的程序修改是一个乏味的、人工的、容易出错的过程。这个项目将调查重复程序修改的范围和性质，并将设计、构建和评估一种新的方法，称为SYDIT，它在应用系统更改时提高开发人员的生产力。在这种方法中，开发人员不再手动应用类似的更改。相反，开发人员提供所选代码的旧版本和新版本作为示例更改，SYDIT将从中概括出可重用的、抽象的、上下文感知的程序转换。(1) SYDIT将计算所选代码的新旧版本之间的程序差异，并通过识别相关数据和控制流上下文以及抽象编辑的内容和位置来创建可重用的编辑脚本。(2)然后SYDIT将自动识别相关候选更改位置，并为每个候选更改生成具体的定制编辑。结合测试和变更影响分析将帮助开发人员验证建议的变更。SYDIT的新差异增量分析将帮助开发人员了解移植更改在每个目标上下文中的影响。(3)使用大量的项目历史语料库，本项目将调查重复变更的频率和类型。结果数据集将用于评估SYDIT的准确性和能力，并评估SYDIT可以实现的生产力增益。这项研究的影响将大大提高开发人员在发展大型软件系统中的生产力。通过帮助开发人员将更改彻底地应用到类似的上下文中，并检查建议更改的效果，SYDIT将减少遗漏的错误，并将开发人员从繁琐的、容易出错的手工编辑中解放出来。实证研究将扩展我们对软件进化过程中程序重复变化的理解。

Software systems evolve. Developers must add features, fix bugs, and rewrite software systems to provide better functionality and higher performance. Existing systems also need to migrate to new hardware, computing environments, programming models, and libraries. There exist redundancies, inefficiencies, and error-proneness in the way that we evolve software systems today. In particular, recent empirical studies indicate that developers often apply similar but not identical changes to similar contexts. Making such systematic, repetitive program modifications is a tedious, manual, error-prone process.This project will investigate the extent and nature of repetitive program modifications and will design, build, and evaluate a novel approach, called SYDIT, which improves developer productivity in applying systematic changes. In this approach, developers no longer apply similar changes manually. Instead, developers provide the old and new version of selected code as an example change, and SYDIT will generalize a reusable, abstract, context-aware program transformation from it.(1) SYDIT will compute program differences between the old and new version of selected code and create a reusable edit script by identifying relevant data and control flow context and by abstracting the edits' content and position.(2) SYDIT will then automatically identify related candidate change locations and produce concrete, customized edits to each candidate.Incorporation with testing and change impact analysis will help developers validate suggested changes. SYDIT's new differential delta analysis will help developers understand the effect of ported changes in each target context.(3) Using a large corpus of project histories, this project will investigate the frequency and types of repetitive changes. The resulting data set will be used to evaluate SYDIT's accuracy and capability and to assess a productivity gain that can be achieved by SYDIT.The impact of this research will be substantially improved developer productivity in evolving large software systems. By helping developers apply changes to similar contexts exhaustively and inspect the effect of suggested changes, SYDIT will reduce errors of omission and relieve developers from tedious, error-prone hand editing. The empirical studies will expand our understanding of repetitive program changes during software evolution.