Empirical analysis of programming language adoption

Some programming languages become widely popular while others fail to grow beyond their niche or disappear alto-gether. This paper uses survey methodology to identify the factors that lead to language adoption. We analyze large datasets, including over 200,000 SourceForge projects, 590,000 projects tracked by Ohloh, and multiple surveys of 1,000-13,000 programmers. We report several prominent findings. First, language adoption follows a power law; a small number of languages account for most language use, but the programming mar-ket supports many languages with niche user bases. Second, intrinsic features have only secondary importance in adop-tion. Open source libraries, existing code, and experience strongly influence developers when selecting a language for a project. Language features such as performance, reliability, and simple semantics do not. Third, developers will steadily learn and forget languages, and the overall number of lan-guages developers are familiar with is independent of age. Developers select more varied languages if their education exposed them to different language families. Finally, when considering intrinsic aspects of languages, developers priori-tize expressivity over correctness. They perceive static types as more valuable for properties such as the former rather than for correctness checking. 1