Can static type systems speed up programming? An experimental evaluation of static and dynamic type systems
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Programming languages that use the object-oriented approach have been around for quite a while now. Most of them use either a static or a dynamic type system. However, both types are very common in the industry. But, in spite of their common use in science and practice, only very few scientific studies have tried to evaluate the two type systems' usefulness in certain scenarios. There are arguments for both systems. For example, static type systems are said to aid the programmer in the prevention of type errors, and further, they provide documentation help for, there is an explicit need to ann...
Programming languages that use the object-oriented approach have been around for quite a while now. Most of them use either a static or a dynamic type system. However, both types are very common in the industry. But, in spite of their common use in science and practice, only very few scientific studies have tried to evaluate the two type systems' usefulness in certain scenarios. There are arguments for both systems. For example, static type systems are said to aid the programmer in the prevention of type errors, and further, they provide documentation help for, there is an explicit need to annotate variables and methods with their respective types.This book describes a controlled experiment that was conducted to shed some light into the presented matter. Which of the type systems can live up to its promises? Is one of these better suited for a particular task? And which type system is the most supportive in a problem solving? The main hypothesis claims that a static type system is faster in a problem solving in use of an undocumented API. Thus, in the study, the participants need to solve different programming tasks in an undocumented API environment with the help of the static type system (Java), and the dynamic type system (Groovy).The author starts with a short introduction to the topic, the experimentation, and the motivation. Then, he describes a list of related works, and proceeds to the description of the experiment, its evaluation, and finally, the discussion of the results. This book should prove interesting reading for anyone who is interested in the mechanics that drive programmer productivity and performance that depend on the kind of technology used, as well as for anyone who might be interested in empirical research in software engineering, in general.
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