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 a reasoning system for inferring dimension information in spreadsheets. This system can be used to check the consistency of spreadsheet formulas and can be employed to detect errors in spreadsheets. We have prototypically implemented the system as an add-in to Excel. In an evaluation of this implementation we were able to detect dimension errors in almost 50 % of the investigated spreadsheets, which shows (i) that the system works reliably in practice and (ii) that dimension information can be well exploited to uncover errors in spreadsheets.

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

We present a reasoning system for inferring dimension information in spreadsheets. This system can be used to check the consistency of spreadsheet formulas and thus is able to detect errors in spreadsheets. Our approach is based on three static analysis components. First, the spatial structure of the spreadsheet is analyzed to infer a labeling relationship among cells. Second, cells that are used as labels are lexically analyzed and mapped to potential dimensions. Finally, dimension information is propagated through spreadsheet formulas. An important aspect of the rule system defining dimension inference is that it works bi-directionally, that is, not only "downstream " from referenced arguments to the current cell, but also "upstream " in the reverse direction. This flexibility makes the system robust and turns out to be particularly useful in cases when the initial dimension information that can be inferred from headers is incomplete or ambiguous. We have implemented a prototype system as an add-in to Excel. In an evaluation of this implementation we were able to detect dimension errors in almost 50 % of the investigated spreadsheets, which shows (i) that the system works reliably in practice and (ii) that dimension information can be well exploited to uncover errors in spreadsheets.

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.

Labels in spreadsheets can be exploited for finding errors in spreadsheet formulas. Previous approaches have either used the positional information of labels or their interpretation as dimension for checking the consistency of formulas. In this paper we demonstrate how these two approaches can be combined. We have formalized a combined reasoning system and have implemented a corresponding prototype system. We have evaluated the system on the EUSES spreadsheet corpus. The evaluation has demonstrated that adding a syntactic, spatial analysis to a dimension inference can significantly improve the rate of detected errors. 1.

Abstract approved:

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

Contents lists available at ScienceDirect

Labels in spreadsheets can be exploited for finding formula errors in two principally different ways. First, the spatial relationships between labels and other cells express simple constraints on the cells usage in formulas. Second, labels can be interpreted as units of measurements to provide semantic information about the data being combined in formulas, which results in different kinds of constraints. In this paper we demonstrate how both approaches can be combined into an integrated analysis, which is able to find significantly more errors in spreadsheets than each of the individual approaches. In particular, the integrated system is able to detect errors that cannot be found by either of the individual approaches alone, which shows that the integrated system provides an added value beyond the mere combination of its parts. We also compare the effectiveness of this combined approach with several other conceivable combinations of the involved components and identify a system that seems most effective to find spreadsheet formula errors based on label and unit-of-measurement information.