Spreadsheet systems are live programming environments. Both the data and the code are right in front you, and if you edit either of them, the effects are immediately visible. Unfortunately, spreadsheets lack mechanisms for abstraction, such as classes, function definitions etc. Programming languages excel at abstraction, but most mainstream languages or integrated development environments (IDEs) do not support the interactive, live feedback loop of spreadsheets. As a result, exploring and testing of code is cumbersome and indirect. In this paper we propose a method to bring both worlds closer together, by juxtaposing ordinary code and spreadsheet-like grids in the IDE, called TrueGrid. Using TrueGrid spreadsheet cells can be programmed with a fully featured programming language. Spreadsheet users then may enjoy benefits of source code, including added abstractions, syntax highlighting, version control, etc. On the other hand, programmers may leverage the grid for interactive exploring and testing of code. We illustrate these benefits using a prototype implementation of True- Grid that runs in the browser and uses Javascript as a programming language.@en

In this paper we explore opportunities, challenges, and obstacles that Functional Size Measurement (FSM) experts assume to be in automatically derived functional size, directly from the software project code itself. We designed a structured survey, that was answered by 336 FSM specialists. A majority of the respondents consider FSM to be an important tool for decision making. No indications are found for any perceived impact of agile methodology on the difficulty of applying FSM. Respondents overall think of automated FSM as important, but also difficult to realize. 54% of the respondents think that automated FSM will help measurement specialists, while 44% thinks that it will help decision makers too. The most preferred FSM method for automation is COSMIC (25%), followed by IFPUG (21%) and Nesma (16%). Respondents perceive automated FSM to be most suitable for baselining, benchmarking, and maintenance and legacy purposes.

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Language workbenches are environments for simplifying the creation and use of computer languages. The annual Language Workbench Challenge (LWC) was launched in 2011 to allow the many academic and industrial researchers in this area an opportunity to quantitatively and qualitatively compare their approaches. We first describe all four LWCs to date, before focussing on the approaches used, and results generated, during the third LWC. We give various empirical data for ten approaches from the third LWC. We present a generic feature model within which the approaches can be understood and contrasted. Finally, based on our experiences of the existing LWCs, we propose a number of benchmark problems for future LWCs.

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