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Computational scientists developing software for HPC systems face unique software engineering issues. Attempts to transfer SE technologies to this domain must take these issues into account.
Computational scientists face many challenges when developing software that runs on large-scale parallel machines. However, software-engineering researchers haven't studied their software development processes in much detail. To better understand the nature of software development in this context, the authors examined five large-scale computational science… (More)
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)
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. However, while they often develop useful code, many scientists and engineers are frequently unaware of how various software engineering practices can help them write better code. This article presents the results of a survey on this topic.
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)
Modern software-based services are implemented as distributed systems with complex behavior and failure modes. Many large tech organizations are using experimentation to verify such systems' reliability. Netflix engineers call this approach chaos engineering. They've determined several principles underlying it and have used it to run experiments. This… (More)