SHF: Small: Synthesizing Human-Readable Documentation

点击翻译按钮获取中文摘要

Developing and maintaining software is a key challenge in computer science,with failures costing up to one half of one percent of the US GDP eachyear. Most code is retained and evolved, rather than created from scratch,and professional software developers spend over three-fourths of their timetrying to understand existing code. Understandability and documentationhave become key components of software quality, yet they remain poorlyunderstood by both researchers and practitioners. In a future where thesoftware engineering focus shifts from implementation to design andcomposition concerns, program understandability will become even moreimportant. This research develops tools and techniques for mechanicallygenerating documentation to help make programs easier to understand.The research follows the insight that modern analysis techniques can formrich descriptive models of programs that are both precise and succinct.Human-readable documentation can then be synthesized from such models.The approach applies to large programs across multiple application domains.The research focuses on documenting how code should be used correctly, acritical aspect in an era of components-of-the-shelf development, as wellas documenting how code has changed and evolved over time, a keypart of software maintenance. The research leverages program analysistechniques, machine learning, and textual synthesis, with resultsdisseminated through academic publication; the education, training andmentoring of students; as well as freely-available, open-source tools.