A Comparative Study of Programming Languages in Rosetta Code

Sometimes debates on programming languages are more religious than scientific. Questions about which language is more succinct or efficient, or makes developers more productive are discussed with fervor, and their answers are too often based on anecdotes and unsubstantiated beliefs. In this study, we use the largely untapped research potential of Rosetta Code, a code repository of solutions to common programming tasks in various languages, to draw a fair and well-founded comparison. Rosetta Code offers a large data set for analysis. Our study is based on 7087 solution programs corresponding to 745 tasks in 8 widely used languages representing the major programming paradigms (procedural: C and Go; object-oriented: C# and Java; functional: F# and Haskell; scripting: Python and Ruby). Our statistical analysis reveals, most notably, that: functional and scripting languages are more concise than procedural and object-oriented languages; C is hard to beat when it comes to raw speed on large inputs, but performance differences over inputs of moderate size are less pronounced and allow even interpreted languages to be competitive; compiled strongly-typed languages, where more defects can be caught at compile time, are less prone to runtime failures than interpreted or weakly-typed languages. We discuss implications of these results for developers, language designers, and educators

Examining the Expert Gap in Parallel Programming

Abstract. Parallel programming is often regarded as one of the hardest programming disciplines. On the one hand, parallel programs are notoriously prone to concurrency errors; and, while trying to avoid such errors, achieving program performance becomes a significant challenge. As a result of the multicore revolution, parallel programming has however ceased to be a task for domain experts only. And for this reason, a large variety of languages and libraries have been proposed that promise to ease this task. This paper presents a study to investigate whether such approaches succeed in closing the gap between domain experts and mainstream developers. Four approaches are studied: Chapel, Cilk, Go, and Threading Building Blocks (TBB). Each approach is used to implement a suite of benchmark programs, which are then reviewed by notable experts in the language. By comparing original and revised versions with respect to source code size, coding time, execution time, and speedup, we gain insights into the importance of expert knowledge when using modern parallel programming approaches.