Journal ArticleDOI
Studying programmer behavior experimentally: the problems of proper methodology
TLDR
Three major areas of methodological concern, the selection of subjects, materials, and measures, are reviewed and the first two of these areas continue to present major difficulties for this type of research.Abstract:
The application of behavioral or psychological techniques to the evaluation of programming languages and techniques is an approach which has found increased applicability over the past decade. In order to use this approach successfully, investigators must pay close attention to methodological issues, both in order to insure the generalizability of their findings and to defend the quality of their work to researchers in other fields. Three major areas of methodological concern, the selection of subjects, materials, and measures, are reviewed. The first two of these areas continue to present major difficulties for this type of research.read more
Citations
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Journal ArticleDOI
Building knowledge through families of experiments
TL;DR: The paper discusses the experience of the authors, based upon a collection of experiments, in terms of a framework for organizing sets of related studies, with specific emphasis on persistent problems encountered in experimental design, threats to validity, criteria for evaluation, and execution of experiments in the domain of software engineering.
On the Cognitive Effects of Learning Computer Programming: A Critical Look. Technical Report No. 9.
Roy Pea,D. Midian Kurland +1 more
TL;DR: This paper critically examines current thinking about whether learning computer programming promotes the development of general higher mental functions, and shows how the available evidence, and the underlying assumptions about the process of learning to program, fail to address this issue adequately.
Journal ArticleDOI
On the cognitive effects of learning computer programming
Roy Pea,D. Midian Kurland +1 more
TL;DR: In this article, a developmental cognitive science perspective on learning to program is presented, incorporating developmental and cognitive science considerations of the mental activities involved in programming, highlighting the importance for future research of investigating students' interactions with instructional and programming contexts, developmental transformations of their programming skills, and their background knowledge and reasoning abilities.
Journal ArticleDOI
Expertise in debugging computer programs: A process analysis
TL;DR: This paper reports the results of an exploratory study that investigated expert and novice debugging processes with the aim of contributing to a general theory of programming expertise.
Journal ArticleDOI
Programming as theory building
TL;DR: It is concluded that the proper, primary aim of programming is to have the programmers build theories of the manner in which the problems at hand are solved by program execution.
References
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Book
Statistical Principles in Experimental Design
TL;DR: In this article, the authors introduce the principles of estimation and inference: means and variance, means and variations, and means and variance of estimators and inferors, and the analysis of factorial experiments having repeated measures on the same element.
Journal ArticleDOI
Statistical Principles in Experimental Design
TL;DR: This chapter discusses design and analysis of single-Factor Experiments: Completely Randomized Design and Factorial Experiments in which Some of the Interactions are Confounded.
Book
Human Problem Solving
TL;DR: The aim of the book is to advance the understanding of how humans think by putting forth a theory of human problem solving, along with a body of empirical evidence that permits assessment of the theory.
Book
A complexity measure
TL;DR: In this paper, a graph-theoretic complexity measure for managing and controlling program complexity is presented. But the complexity is independent of physical size, and complexity depends only on the decision structure of a program.
Journal ArticleDOI
A Complexity Measure
TL;DR: Several properties of the graph-theoretic complexity are proved which show, for example, that complexity is independent of physical size and complexity depends only on the decision structure of a program.