SHF: Small: S3: Statistical and Structural Analysis for Spreadsheets

SHF：小型：S3：电子表格的统计和结构分析

• 批准号: 1617892
• 负责人: Emery Berger
• 金额: $ 34.74万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617892&HistoricalAwards=false
• 关键词: SHF; Small; S3; Statistical; Structural

Spreadsheets are the most commonly used programming environment in the world; there are more than 750 million users of Microsoft Excel alone. Spreadsheets are widely used in government, scientific, and financial settings; over 95% of US firms use them for financial reporting and 85% use them for budgeting and forecasting. Unfortunately, errors are endemic to spreadsheets; a recent study found an error rate of over 95%. Spreadsheet errors have had catastrophic consequences, leading to losses of billions of dollars. This project uses automatic analysis techniques designed specifically for spreadsheets to (a) automatically detect and help prevent errors in spreadsheets, dramatically increasing the reliability of their calculations, (b) reduce the risks of serious mistakes, and (c) potentially save the economy millions if not billions of dollars.This project develops statistical and structural analyses for spreadsheets (S3). Spreadsheets have unique features that make them different from standard programming languages, and thus demand new program analyses that exploit their characteristics. S3 employs statistical analyses over the spatial and deep structure of spreadsheet formulas to identify spreadsheet cells that are highly anomalous and thus likely to be wrong. S3 reduces the problem of finding data and formula errors to that of finding anomalous structures via a novel vector representation that combines spatial and structural information (including patterns of dependencies). Applying statistical analyses across these vectors can then identify formulas that are highly unusual in any dimension, and thus likely to be wrong. S3 operates both at an individual spreadsheet level and also incorporates learned models of spreadsheet usage from large bodies of existing spreadsheets to condition the analysis and further reduce false positive rates.

电子表格是世界上最常用的编程环境；仅 Microsoft Excel 就有超过 7.5 亿用户。电子表格广泛应用于政府、科学和金融领域；超过 95% 的美国公司使用它们进行财务报告，85% 使用它们进行预算和预测。不幸的是，错误在电子表格中很常见。最近的一项研究发现错误率超过 95%。电子表格错误已造成灾难性后果，导致数十亿美元的损失。该项目使用专门为电子表格设计的自动分析技术，以（a）自动检测并帮助防止电子表格中的错误，从而显着提高计算的可靠性，（b）降低严重错误的风险，以及（c）可能为经济节省数百万甚至数十亿美元。该项目为电子表格开发统计和结构分析（S3）。电子表格具有不同于标准编程语言的独特功能，因此需要利用其特性的新程序分析。 S3 对电子表格公式的空间和深层结构进行统计分析，以识别高度异常且可能错误的电子表格单元格。 S3 通过结合空间和结构信息（包括依赖性模式）的新颖向量表示，将查找数据和公式错误的问题简化为查找异常结构的问题。对这些向量应用统计分析可以识别在任何维度上都非常不寻常的公式，因此可能是错误的。 S3 不仅在单个电子表格级别运行，还结合了从大量现有电子表格中学习到的电子表格使用模型，以调节分析并进一步降低误报率。