This paper describes the design and implementation of a unit and header inference system for spreadsheets. The system is based on a formal model of units that we have described in previous work. Since the unit inference depends on information about headers in a spreadsheet, a realistic unit inference system requires a method for automatically determining headers. The present paper describes (1) several spatial-analysis algorithms for header inference, (2) a framework that facilitates the integration of different algorithms, and (3) the implementation of the system. The combined header and unit inference system is fully integrated into Microsoft Excel and can be used to automatically identify various kinds of errors in spreadsheets. Test results show that the system works accurately and reliably. 1

Despite their ability to help with program correctness, assertions have been notoriously unpopular---even with professional programmers. End-user programmers seem even less likely to appreciate the value of assertions; yet end-user programs suffer from serious correctness problems that assertions could help detect. This leads to the following question: can end users be enticed to enter assertions? To investigate this question, we have devised a curiosity-centered approach to eliciting assertions from end users, built on a surprise-explain-reward strategy. Our follow-up work with end-user participants shows that the approach is effective in encouraging end users to enter assertions that help them find errors.

We introduce a visual specification language for spreadsheets that allows the definition of spreadsheet templates. A spreadsheet generator can automatically create Excel spreadsheets from these templates together with customized update operations. It can be shown that spreadsheets created in this way are free from a large class of errors, such as reference, omission, and type errors. We present a formal definition of the visual language for templates and describe the process of generating spreadsheets from templates. In addition, we present an editor for templates and analyze the editor using the Cognitive Dimensions framework. 1

Spreadsheets are widely used in all kinds of business applications. Numerous studies have shown that they contain many errors that sometimes have dramatic impacts. One reason for this situation is the low-level, cell-oriented development process of spreadsheets. We improve this process by introducing and formalizing a higher-level object-oriented model termed ClassSheet. While still following the tabular look-and-feel of spreadsheets, ClassSheets allow the developer to express explicitly business object structures within a spreadsheet, which is achieved by integrating concepts from the UML (Unified Modeling Language). A stepwise automatic transformation process generates a spreadsheet application that is consistent with the ClassSheet model. Thus, by deploying the formal underpinning of ClassSheets, a large variety of errors can be prevented that occur in many existing spreadsheet applications today. The presented ClassSheet approach links spreadsheet applications to the object-oriented modeling world and advocates an automatic model-driven development process for spreadsheet applications of high quality.

Programmers often omit input validation when inputs can appear in many different formats or when validation criteria cannot be precisely specified. To enable validation in these situations, we present a new technique that puts valid inputs into a consistent format and that identifies “questionable ” inputs which might be valid or invalid, so that these values can be double-checked by a person or a program. Our technique relies on the concept of a “tope”, which is an application-independent abstraction describing how to recognize and transform values in a category of data. We present our definition of topes and describe a development environment that supports the implementation and use of topes. Experiments with web application and spreadsheet data indicate that using our technique improves the accuracy and reusability of validation code and also improves the effectiveness of subsequent data cleaning such as duplicate identification.

Based on 1) research into mutation testing for general-purpose programming languages and 2) spreadsheet errors that have been reported in the literature, we have developed a suite of mutation operators for spreadsheets. We present an evaluation of the mutation adequacy of definition-use adequate test suites generated by a constraint-based automatic test-case generation system we have developed in previous work. The results of the evaluation suggest additional constraints that can be incorporated into the system to target mutation adequacy. In addition to being useful in mutation testing of spreadsheets, the operators can be used in the evaluation of error-detection tools and also for seeding spreadsheets with errors for empirical studies. We describe two case studies where the suite of mutation operators helped us carry out such empirical evaluations. The main contribution of this paper is a suite of mutation operators for spreadsheets that can be used for performing empirical evaluations of spreadsheet tools to indicate ways in which the tools can be improved.

We present an error-detection and-correction approach for spreadsheets that automatically generates questions about input/output pairs and, depending on the feedback given by the user, proposes changes to the spreadsheet that would correct detected errors. This approach combines and integrates previous work on automatic test-case generation and goal-directed debugging. We have implemented this method as an extension to MS Excel. We carried out an evaluation of the system using spreadsheets seeded with faults using mutation operators. The evaluation shows among other things that up to 93 % of the first-order mutants and 98 % of the second-order mutants were detected by the system using the automatically generated test cases. 1.

Most programs today are written not by professional software developers, but by people with expertise in other domains working towards goals for which they need computational support. For example, a teacher might write a grading spreadsheet to save time grading, or an interaction designer might use an interface builder to test some user interface design ideas. Although these end-user programmers may not have the same goals as professional developers, they do face many of the same software engineering challenges, including understanding their requirements, as well as making decisions about design, reuse, integration, testing, and debugging. This article summarizes and classifies research on these activities, defining the area of End-User Software Engineering (EUSE) and related terminology. The article then discusses empirical research about end-user software engineering activities and the technologies designed to support them. The article also addresses several crosscutting issues in the design of EUSE tools, including the roles of risk, reward, and domain complexity, and self-efficacy

Spreadsheets are widely used, and studies have shown that most end-user spreadsheets contain non-trivial errors. Most of the currently available tools that try to mitigate this problem require varying levels of user intervention. This paper presents a system, called UCheck, that detects errors in spreadsheets automatically. UCheck carries out automatic header and unit inference, and reports unit errors to the users. UCheck is based on two static analyses phases that infer header and unit information for all cells in a spreadsheet. We have tested UCheck on a wide variety of spreadsheets and found that it works accurately and reliably. The system was also used in a continuing education course for high school teachers, conducted through Oregon State University, aimed at making the participants aware of the need for quality control in the creation of spreadsheets.

The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be debugged. Providing ways for the user to debug it matters, because without the ability to fix errors users may find that the learned program's errors are too damaging for them to be able to trust such programs. We present a new approach to enable end users to debug a learned program. We then use an early prototype of our new approach to conduct a formative study to determine where and when debugging issues arise, both in general and also separately for males and females. The results suggest opportunities to make machine-learned programs more effective tools. ACM Classification: H5.2. Information interfaces and presentation (e.g., HCI): User Interfaces