作者: Emitza Guzman , Mohamed Ibrahim , Martin Glinz
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摘要: Twitter messages (tweets) contain important information for software and requirements evolution, such as feature requests, bug reports shortcoming descriptions. For this reason, is an source crowd-based engineering evolution. However, a manual analysis of unfeasible due to the large number tweets, its unstructured nature varying quality. Therefore, automatic techniques are needed for, e.g., summarizing, classifying prioritizing tweets. In work we present survey with 84 practitioners researchers that studies tweet attributes most telling priority when performing evolution tasks. We believe our results can be used implement mechanisms user feedback social components. Thus, it helpful enhancing