CAREER: Analysis and Automation of Systematic Software Modifications

软件系统在进化。开发人员必须添加功能、修复错误并重写软件系统，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、低效和容易出错的问题。特别是，最近的经验研究表明，开发人员经常对类似的上下文应用类似但不相同的更改。进行这种系统化、重复性的程序修改是一个乏味的、手动的、容易出错的过程。这个项目将调查重复性程序修改的范围和性质，并将设计、构建和评估一种称为SYDIT的新方法，它可以提高开发人员应用系统更改的生产率。在这种方法中，开发人员不再手动应用类似的更改。相反，开发人员提供选定代码的旧版本和新版本作为示例更改，SYDIT将根据它概括可重用的、抽象的、上下文感知的程序转换。(1)SYDIT将计算选定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通过抽象编辑的内容和位置来创建可重用的编辑脚本。(2)SYDIT然后将自动识别相关的候选更改位置，并对每个候选进行具体的、定制的编辑。与测试和更改影响分析相结合将帮助开发人员验证建议的更改。SYDIT新的差异增量分析将帮助开发人员了解每个目标上下文中移植更改的影响。(3)使用大量项目历史语料库，本项目将调查重复更改的频率和类型。生成的数据集将用于评估SYDIT的准确性和能力，并评估SYDIT可以实现的生产率提高。这项研究的影响将显著提高大型软件系统发展中的开发人员生产率。通过帮助开发人员将更改详尽地应用到类似的上下文中，并检查建议更改的效果，SYDIT将减少遗漏错误，并将开发人员从乏味的、容易出错的手动编辑中解放出来。这些实证研究将扩大我们对软件演化过程中重复程序变化的理解。

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.