Spreadsheets are popular end user programming tools, especially in the industrial world. This makes them interesting research targets. However, there does not exist a reliable grammar that is concise enough to facilitate formula parsing and analysis and to support research on spreadsheet codebases. This paper presents a grammar for spreadsheet formulas that can successfully parse 99.99% of more than 8 million unique formulas extracted from 4 spreadsheet datasets. Our grammar is compatible with the spreadsheet formula language, recognizes the spreadsheet formula elements that are required for supporting spreadsheets research, and produces parse trees aimed at further manipulation and analysis. Additionally, we use the grammar to analyze the characteristics of the formulas of the 4 datasets in 3 different dimensions: complexity, functionality, and data utilization. Our results show that (1) most Excel formulas are simple, however formulas with more than 50 functions or operations exist, (2) almost all formulas use data from other cells, which is often not local, and (3) a surprising number of referring mechanisms are used by less than 1% of the formulas.

Spreadsheets can be considered to be the world's most successful end-user programming language. In fact, one could say spreadsheets are programs. This paper starts with a comparison of spreadsheets to software: spreadsheets are similar in terms of applications domains, expressive power and maintainability problems. We then reflect upon what makes spreadsheets successful: liveness, directness and an easy deployment environment seem contribute largely to their success. Being a programming language, several techniques from software engineering can be applied to spreadsheets. We present an overview of such research directions, including spreadsheet testing, reverse engineering, smell detection, clone detection and refactoring. Finally, open challenges and future plans for the domain of spreadsheet software engineering are presented.

Code smells were originally designed for object-oriented code, but in recent years, have been applied to end-user languages, including spreadsheets and Yahoo! Pipes. In this paper, we explore code smells in block-based end-user programming languages aimed at education. Specifically, we explore the occurrence of smells in two educational languages not previously targeted by smell detection and refactoring research: LEGO MINDSTORMS EV3 and Microsoft's Kodu. The results of this exploration show that object-oriented-inspired smells indeed occur in educational end-user languages and are present in 88% and 93% of the EV3 and Kodu programs, respectively. Most commonly we find that programs are plagued with lazy class, duplication, and dead code smells, with duplication smells being present in nearly two-thirds of programs in both languages.