NDLI: Adressing problems with external validity of repository mining studies through a smart data platform

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Adressing problems with external validity of repository mining studies through a smart data platform

Content Provider	ACM Digital Library
Author	Grabowski, Jens Trautsch, Fabian Herbold, Steffen Makedonski, Philip
Abstract	Research in software repository mining has grown considerably the last decade. Due to the data-driven nature of this venue of investigation, we identified several problems within the current state-of-the-art that pose a threat to the external validity of results. The heavy re-use of data sets in many studies may invalidate the results in case problems with the data itself are identified. Moreover, for many studies data and/or the implementations are not available, which hinders a replication of the results and, thereby, decreases the comparability between studies. Even if all information about the studies is available, the diversity of the used tooling can make their replication even then very hard. Within this paper, we discuss a potential solution to these problems through a cloud-based platform that integrates data collection and analytics. We created the prototype SmartSHARK that implements our approach. Using SmartSHARK, we collected data from several projects and created different analytic examples. Within this article, we present SmartSHARK and discuss our experiences regarding the use of SmartSHARK and the mentioned problems.
Starting Page	97
Ending Page	108
Page Count	12
File Format	PDF
ISBN	9781450341868
DOI	10.1145/2901739.2901753
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Software analytics Smart data Software mining
Content Type	Text
Resource Type	Article

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