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Researchers often analyze several revisions of a software project to obtain historical data about its evolution. For example, they statically analyze the source code and monitor the evolution of certain metrics over multiple revisions. The time and resource requirements for running these analyses often make it necessary to limit the number of analyzed revisions, e.g., by only selecting major revisions...
More than other machine learning techniques, neural networks have been shown to excel at tasks where humans traditionally outperform computers: recognizing objects in images, distinguishing spoken words from background noise or playing "Go". These are hard problems, where hand-crafting solutions is rarely feasible due to their inherent complexity. Higher level program comprehension is not...
Continuous Delivery (CD) enables mobile developers to release small, high quality chunks of working software in a rapid manner. However, faster delivery and a higher software quality do neither guarantee user satisfaction nor positive business outcomes. Previous work demonstrates that app reviews may contain crucial information that can guide developer's software maintenance efforts to obtain higher...
Software engineering research often requires analyzing multiple revisions of several software projects, be it to make and test predictions or to observe and identify patterns in how software evolves. However, code analysis tools are almost exclusively designed for the analysis of one specific version of the code, and the time and resources requirements grow linearly with each additional revision to...
Existing code- and software evolution studies typically operate on the scale of a few revisions of a small number of projects, mostly because existing tools are unsuited for performing large-scale studies. We present a novel approach, which can be used to analyze an arbitrary number of revisions of a software project simultaneously and which can be adapted for the analysis of mixed-language projects...
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