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In software engineering, bots range from simple automated scripts to decision-making autonomous systems. The spectrum of applications of bots in software engineering is so wide and diverse, that a comprehensive overview and categorization of such bots is needed. Existing works considered selective bots to be analyzed and failed to provide the overall picture. Hence it is significant to categorize bots in software engineering through analyzing why, what and how the bots are applied in software engineering. We approach the problem with a systematic mapping study based on the research articles published in this topic. This study focuses on classification of bots used in software engineering, the various dimensions of the characteristics, the more frequently researched area, potential research spaces to be explored and the perception of bots in the developer community. This study aims to provide an introduction and a broad overview of bots used in software engineering. 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In the past, however, they were not always perceived positively and sometimes experienced a negative bias compared to their human counterparts. We conducted a qualitative study in which we deployed an autonomous refactoring bot for 41 days in a student software development project. In between and at the end, we conducted semi-structured interviews to find out how developers perceive the bot and whether they are more or less critical when reviewing the contributions of a bot compared to human contributions. Our findings show that the bot was perceived as a useful and unobtrusive contributor, and developers were no more critical of it than they were about their human colleagues, but only a few team members felt responsible for the bot.", "isbn" : "978-989-758-395-7", "doi" : "10.5220/0009168803030310", "bibtexKey": "wyrich2020perception" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cca5768297133b20968231ce07e0f735/droessler", "tags" : [ "bots","data","facebook","media","meinungsbildung","mining","political","politik","social","twitter" ], "intraHash" : "cca5768297133b20968231ce07e0f735", "interHash" : "2ab7825052e40b418a507898ec0f72bd", "label" : "Schrott im Netz. 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