Predicting software engineering trends is a strategically important asset for both developers and managers, but it's also difficult, due to the wide range of factors involved and the complexity of their interactions. This paper reveals some interesting trends and a method for studying other important software engineering trends. This article trades breadth for depth by focusing on a small, compact set of trends involving 17 high-level programming languages. We quantified many of their relevant factors, and then collected data on their evolution over 10 years. By applying statistical methods to this data, we aim to gain insight into what does and does not make a language successful.