HCC: Small: Code Stories: Linking Code Influences and Changes in Code Histories

计算机软件几乎涵盖了现代生活的方方面面。与其他形式的基础设施一样，我们维护软件以保持其可靠运行是至关重要的。然而，维护是昂贵的：今天，维护和更新一款软件的过程占开发总成本的50%以上。与维护软件相关的大部分困难和成本都是由于程序员为了理解现有的计算机代码而付出的努力。特别是，可能很难确定程序的哪些部分负责哪些行为，以及特定实现细节的原因。然而，这些问题的线索在最初创建软件时就已经存在：错误消息、访问的网页、编辑痕迹和内部文档都将软件、目标和原因联系在一起。这个项目的目标是开发自动捕获各种上下文信息的方法，以支持未来的软件维护，并以帮助未来程序员理解和维护代码的方式呈现代码历史。为了实现这一目标，该项目采用以用户为中心的方法，首先了解代码历史，然后开发方法来捕获并向代码库的未来用户呈现这些信息。项目团队将首先进行观察性研究，以确定程序员如何自然地感知和组织他们创建的代码的历史。这些研究的结果将为原型代码历史(称为代码故事)的设计提供信息，这些原型代码历史将通过用户测试进一步细化，从而能够识别有效代码历史的属性。观察性研究还将指导关于要捕获哪些类型的代码更改和上下文信息(例如，信息搜寻活动和错误消息)的决策。与此同时，该团队将开发启发式算法，将事件和信息分割成有语义意义的块。该团队将从两个方面对这项工作进行评估。第一个评估将比较程序员在尝试使用构造良好的GIT历史(通过保留代码库的版本来维护历史的当前最佳实践)修改不熟悉的代码库时的效率与代码故事。第二次评估将通过观察和采访捕获程序员指导的代码故事版本，并将这些研究产生的手动编写的代码故事与开发的工具创建的自动代码故事进行比较。这一比较的结果将有助于确定未来代码历史研究的优先事项，并为未来的程序员教育提供有用的资源。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Computer software underlies nearly every aspect of modern life. Like other forms of infrastructure, it is critical that we maintain software to keep it functioning reliably. However, maintenance is expensive: today, the process of maintaining and updating a piece of software accounts for more than 50 percent of the total cost of developing it. Much of the difficulty and cost associated with maintaining software is due to the effort programmers exert to understand the existing computer code. In particular, it can be hard to determine which parts of a program are responsible for what behaviors, along with the reasons for particular implementation details. However, clues to these problems were present when the software was originally created: error messages, visited web pages, editor traces, and internal documentation all tie together software, goals, and reasons. The goal of this project is to develop ways to automatically capture kinds of contextual information that would support future software maintenance and to present this code history in ways that help future programmers understand and maintain that code.To realize this goal, this project takes a user-centered approach to first understanding code histories and then developing ways to capture and present this information for future users of a codebase. The project team will first conduct observational studies to determine how programmers naturally perceive and organize the history of the code they created. The results from these studies will inform the design of prototype code histories (called Code Stories) that will be further refined through user testing, enabling identification of the properties of effective code histories. The observational studies will also guide decisions about what kinds of code changes and contextual information (e.g., information foraging activity and error messages) to capture. In parallel, the team will develop heuristics for segmenting events and information into semantically meaningful chunks. The team will evaluate the work in two ways. The first evaluation will compare programmer efficiency when attempting to modify an unfamiliar codebase using a well-constructed git history (the current best practice that maintains history through keeping versions of a codebase) versus Code Stories. The second evaluation will capture programmer-guided versions of Code Stories through observations and interviews and compare the hand-authored Code Story emerging from these studies with the automatic Code Stories created by the tools developed. The results of this comparison will help to identify priorities for future research in code histories, as well as provide useful resources for future programmer education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.