Users often need to test, debug or reuse spreadsheets. We present a new algorithm that can identify differences between two spreadsheets, providing a basis for future tools to help users compare two versions of a spreadsheet (thereby seeing what is new and needs testing) or two different spreadsheets (thereby seeing which is more appropriate for reuse in a situation). This algorithm, RowColAlign, is a two-dimensional generalization of the classic dynamic programming algorithm for solving the one-dimensional longest common subsequence problem. In addition, we present a new planted model for generating test cases to evaluate this algorithm and others like it, including the greedy SheetDiff algorithm presented in prior work. In our evaluation, our new RowColAlign algorithm made no errors at all on test cases, including test cases comparable to relatively large spreadsheets. Moreover, further analysis revealed that it is unexpected for our new algorithm to make errors except when spreadsheets contain an unrealistically small number of distinct values. These results are extremely encouraging, revealing our algorithm's potential as the basis for future spreadsheet tools.

The Scratch environment exemplifies a tool+community approach to teaching elementary programming skills, as it includes a website where users can publish, discuss, and organize animations that are programs. To explore this environment's effectiveness for helping people to develop programming skills, a quantitative analysis of 250 randomly selected users' data, including more than 1,000 of their animations, was performed. Skill based on 4 models that had proven useful in prior empirical studies was measured. Overall, mixed results about the environment's effectiveness were found. Among users who do not drop out, an increasing progression in social skills was found. However, an extremely high drop-out rate was also observed. Moreover, a flat or decreasing level of demonstrated skill was observed on virtually every measure. These results call into question whether simply combining an animation tool and an online community is sufficient for keeping people engaged long enough to learn elementary programming skills.

Most spreadsheets, like other software, change over time. A frequently occurring scenario is the repeated reuse and adaptation of spreadsheets from one project to another. If several versions of one spreadsheet for grading/ budgeting/etc. have accumulated, it is often not obvious which one to choose for the next project. In situations like these, an understanding of how two versions of a spreadsheet differ is crucial to make an informed choice. Other scenarios are the reconciliation of two spreadsheets created by different users, generalizing different spreadsheets into a common template, or simply understanding and documenting the evolution of a spreadsheet over time. In this paper we present a method for identifying the changes between two spreadsheets with the explicit goal of presenting them to users in a concise form. We have implemented a prototype system, called SheetDiff, and tested the approach on several different spreadsheet pairs. As our evaluations will show, this system works reliably in practice. Moreover, we have compared SheetDiff to similar systems that are commercially available. An important difference is that while all these other tools distribute the change representation over two spreadsheets, our system displays all changes in the context of one spreadsheet, which results in a more compact representation.

Spreadsheets have become one of the most widely-adopted software technologies. They have proven useful for performing numeric computations as well as for organizing, manipulating, exploring, and visualizing data. Yet only one aspect of spreadsheets, formulas, has received extensive attention in field studies to date. In this paper, we describe a three-part field study that widens this focus to uncover a broader range of challenges that people encounter when creating and using spreadsheets. This study has revealed several opportunities to improve spreadsheet editors, including developing different modes for spreadsheet creation, improving support for spreadsheet reuse, and helping users to find and use features.

Online games are a rapidly growing Internet application. In order to run a successful online game, game companies and game infrastructure providers must properly manage game workloads and content so that they can maximize player satisfaction while minimizing their own costs. Toward this end, this paper provides a comprehensive, long-term analysis of several popular online games and their players using one of the richest data sets available for online games.

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.

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.

Cheating in on-line games is a prevalent problem for both game makers and players. The popular real-time strategy game genre is especially vulnerable to cheats, as it is frequently hosted as a peer-to-peer game. As the genre has moved towards a distributed simulation approach to gameplay, the number of cheats has been reduced to bug exploits and “maphacks”: a form of information exposure that reveals the opponent’s units and positions when they should be hidden. This paper proposes a technique for detecting maphacking based on bit commitment and explores the tradeoffs in network traffic and information exposure inherent in reducing information exposure in peer-to-peer games.

For many on-line games, user experience is impacted significantly by network latency. As on-line games and on-line game servers proliferate, the ability to discover and connect to nearby servers is essential for maintaining user satisfaction. In this paper, we present a redirection service for on-line games based on the geographic location of players relative to servers. As our results show, the service better meets client demand, saving each client and the Internet as a whole, thousands of miles of networking inefficiency.