With hundreds of millions of users, spreadsheets are one of the most important end-user applications. Spreadsheets are easy to use and allow users great flexibility in storing data. This flexibility comes at a price: users often treat spreadsheets as a poor man's database, leading to creative solutions for storing high-dimensional data. The trouble arises when users need to answer queries with their data. Data manipulation tools make strong assumptions about data layouts and cannot read these ad-hoc databases. Converting data into the appropriate layout requires programming skills or a major investment in manual reformatting. The effect is that a vast amount of real-world data is "locked-in" to a proliferation of one-off formats. We introduce FlashRelate, a synthesis engine that lets ordinary users extract structured relational data from spreadsheets without programming. Instead, users extract data by supplying examples of output relational tuples. FlashRelate uses these examples to synthesize a program in Flare. Flare is a novel extraction language that extends regular expressions with geometric constructs. An interactive user interface on top of FlashRelate lets end users extract data by point-and-click. We demonstrate that correct Flare programs can be synthesized in seconds from a small set of examples for 43 real-world scenarios. Finally, our case study demonstrates FlashRelate's usefulness addressing the widespread problem of data trapped in corporate and government formats.

Spreadsheets store a tremendous amount of important data. One reason spreadsheets are so successful is that they are both easy to use and allow users great expressiveness in storing and manipulating their data. This flexibility comes at a price, as presentation elements are often combined with the underlying data model. As a result, many spreadsheets contain data in ad-hoc formats. These formats complicate the use of traditional relational tools which require data in a normalized form. Normalizing data from these formats is often tedious or requires programming, and often, a user may prefer the original presentation. We describe an approach that allows users to easily extract structured data from spreadsheets without programming. We make two contributions. First, we describe a novel domain specific language called FLARE that extends traditional regular expressions with spatial constraints. Second, we describe an algorithm called FLASHRELATE that can synthesize FLARE programs from user-provided positive and negative examples. Using 43 benchmarks drawn both from a standard spreadsheet corpus and from Excel user-help forums, we demonstrate that correct extraction programs can be synthesized quickly from a small number of examples. Our approach generalizes to many data-cleaning tasks on semi-structured spreadsheets.

Spreadsheets are one of the most widely used programming environments, and are widely deployed in domains like finance where errors can have catastrophic consequences. We present a static analysis specifically designed to find spreadsheet formula errors. Our analysis directly leverages the rectangular character of spreadsheets. It uses an information-theoretic approach to identify formulas that are especially surprising disruptions to nearby rectangular regions. We present ExceLint, an implementation of our static analysis for Microsoft Excel. We demonstrate that ExceLint is fast and effective: across a corpus of 70 spreadsheets, ExceLint takes a median of 5 seconds per spreadsheet, and it significantly outperforms the state of the art analysis.

Humans can perform many tasks with ease that remain difficult or impossible for computers. Crowdsourcing platforms like Amazon's Mechanical Turk make it possible to harness human-based computational power at an unprecedented scale. However, their utility as a general-purpose computational platform remains limited. The lack of complete automation makes it difficult to orchestrate complex or interrelated tasks. Scheduling more human workers to reduce latency costs real money, and jobs must be monitored and rescheduled when workers fail to complete their tasks. Furthermore, it is often difficult to predict the length of time and payment that should be budgeted for a given task. Finally, the results of humanbased computations are not necessarily reliable, both because human skills and accuracy vary widely, and because workers have a financial incentive to minimize their effort. This paper introduces AUTOMAN, the first fully automatic crowdprogramming system. AUTOMAN integrates humanbased computations into a standard programming language as ordinary function calls, which can be intermixed freely with traditional functions. This abstraction lets AUTOMAN programmers focus on their programming logic. An AUTO-MAN program specifies a confidence level for the overall computation and a budget. The AUTOMAN runtime system then transparently manages all details necessary for scheduling, pricing, and quality control. AUTOMAN automatically schedules human tasks for each computation until it achieves the desired confidence level; monitors, reprices, and restarts human tasks as necessary; and maximizes parallelism across human workers while staying under budget.

Testing and static analysis can help root out bugs in programs, but not in data. This paper introduces data debugging , an approach that combines program analysis and statistical analysis to automatically find potential data errors. Since it is impossible to know a priori whether data are erroneous, data debugging instead locates data that has a disproportionate impact on the computation. Such data is either very important, or wrong. Data debugging is especially useful in the context of data-intensive programming environments that intertwine data with programs in the form of queries or formulas. We present the first data debugging tool, CheckCell, an add-in for Microsoft Excel. CheckCell identifies cells that have an unusually high impact on the spreadsheet's computations. We show that CheckCell is both analytically and empirically fast and effective. We show that it successfully finds injected typographical errors produced by a generative model trained with data entry from 169,112 Mechanical Turk tasks. CheckCell is more precise and efficient than standard outlier detection techniques. CheckCell also automatically identifies a key flaw in the infamous Reinhart and Rogoff spreadsheet.