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  • 标题:MIDST: an enhanced development environment thatimproves the maintainability of a data science analysis
  • 本地全文:下载
  • 作者:Jeffrey S.Saltz ; Robert Heckman ; Kevin Crowston
  • 期刊名称:International Journal of Information Systems and Project Management
  • 印刷版ISSN:2182-7796
  • 电子版ISSN:2182-7788
  • 出版年度:2020
  • 卷号:8
  • 期号:3
  • 页码:5-22
  • DOI:10.12821/ijispm080301
  • 语种:English
  • 出版社:SciKA
  • 摘要:With the increasing ability to generate actionable insight from data, the field of data science has seen significant growth.As more teams develop data science solutions, the analytical code they develop will need to be enhanced in the future,by an existing or a new team member. Thus, the importance of being able to easily maintain and enhance the coderequired for an analysis will increase. However, to date, there has been minimal research on the maintainability of ananalysis done by a data science team. To help address this gap, data science maintainability was explored by (1)creating a data science maintainability model, (2) creating a new tool, called MIDST (Modular Interactive Data ScienceTool), that aims to improve data science maintainability, and then (3) conducting a mixed method experiment toevaluate MIDST. The new tool aims to improve the ability of a team member to update and rerun an existing datascience analysis by providing a visual data flow view of the analysis within an integrated code and computationalenvironment. Via an analysis of the quantitative and qualitative survey results, the experiment found that MIDST doeshelp improve the maintainability of an analysis. Thus, this research demonstrates the importance of enhanced tools tohelp improve the maintainability of data science projects.
  • 关键词:project management;data science;maintainability;visual programming;data science development environment
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