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Measuring effort accurately and consistently across subjects in a programming experiment can be a surprisingly difficult task. In particular, measures based on self-reported data may differ significantly from measures based on data which is recorded automatically from a subject's computing environment. Since self-reports can be unreliable, and not all(More)
omputational scientists use computers to simulate physical phenomena in situations where experimentation would be prohibitively expensive or impossible. Advancing scientific research depends on these scien-tists' developing software productively. However, the software development process in this domain differs from other domains. For instance, scientific(More)
Empirical evidence and technology evaluation are needed to close the gap between the state of the art and the state of the practice in software engineering. However, there are difficulties associated with evaluating technologies based on empirical evidence: insufficient specification of context variables, cost of experimentation, and risks associated with(More)
Scientists and engineers devote considerable effort to developing large, complex codes to solve important problems. Our personal experience with such teams suggested that, while they often develop good code, many of these developers are frequently unaware of how various software engineering practices can help them write better code. Our hypothesis is that(More)
In developing High-Performance Computing (HPC) software, time to solution is an important metric. This metric is comprised of two main components: the human effort required developing the software, plus the amount of machine time required to execute it. To date, little empirical work has been done to study the first component: the human effort required and(More)
Context. Writing software for the current generation of parallel systems requires significant programmer effort, and the community is seeking alternatives that reduce effort while still achieving good performance. Objective. Measure the effect of parallel programming models (message-passing vs. PRAM-like) on programmer effort. Design, Setting, and Subjects.(More)