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。仅有超过7.5亿Microsoft Excel的用户。电子表格广泛用于政府，科学和财务环境中；超过95％的美国公司将其用于财务报告，而85％的公司将其用于预算和预测。不幸的是，错误是电子表格特有的。最近的一项研究发现，错误率超过95％。电子表格错误带来了灾难性的后果，导致数十亿美元的损失。该项目使用专门针对电子表格设计的自动分析技术，以自动检测和帮助防止电子表格中的错误，大大提高其计算的可靠性，（b）减少严重错误的风险，（c）可能会节省数十亿美元的经济性，这些项目可能会为统计和结构分析而产生数十亿美元的项目，以开发统计和结构分析（S3）。电子表格具有独特的功能，使其与标准编程语言不同，因此需要进行新的程序分析以利用其特征。 S3对电子表格公式的空间和深层结构进行了统计分析，以识别高度异常且可能是错误的电子表格细胞。 S3将查找数据和公式误差的问题减少到通过结合空间和结构信息（包括依赖性模式）的新型矢量表示来查找异常结构的问题。然后，在这些向量上应用统计分析可以识别在任何维度上高度不寻常的公式，因此可能是错误的。 S3在单独的电子表格级别上运行，还合并了从现有电子表格的大型物体中学习的电子表格使用模型，以调节分析并进一步降低误报率。