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@ARTICLE{Zaytsev:128607,
      author       = {Zaytsev, Yury and Morrison, Abigail},
      title        = {{I}ncreasing quality and managing complexity in
                      neuroinformatics software development with continuous
                      integration.},
      journal      = {Frontiers in neuroinformatics},
      volume       = {6},
      number       = {31},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2013-00341},
      pages        = {1-16},
      year         = {2013},
      abstract     = {High quality neuroscience research requires accurate,
                      reliable and well maintained neuroinformatics applications.
                      As software projects become larger, offering more
                      functionality and developing a denser web of interdependence
                      between their component parts, we need more sophisticated
                      methods to manage their complexity. If complexity is allowed
                      to get out of hand, either the quality of the software or
                      the speed of development suffer, and in many cases both. To
                      address this issue, here we develop a scalable, low-cost and
                      open source solution for continuous integration (CI), a
                      technique which ensures the quality of changes to the code
                      base during the development procedure, rather than relying
                      on a pre-release integration phase. We demonstrate that a
                      CI-based workflow, due to rapid feedback about code
                      integration problems and tracking of code health measures,
                      enabled substantial increases in productivity for a major
                      neuroinformatics project and additional benefits for three
                      further projects. Beyond the scope of the current study, we
                      identify multiple areas in which CI can be employed to
                      further increase the quality of neuroinformatics projects by
                      improving development practices and incorporating
                      appropriate development tools. Finally, we discuss what
                      measures can be taken to lower the barrier for developers of
                      neuroinformatics applications to adopt this useful
                      technique.},
      cin          = {JSC / IAS-6 / JARA-HPC / INM-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      $I:(DE-82)080012_20140620$ / I:(DE-Juel1)INM-6-20090406},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / HASB - Helmholtz Alliance on Systems
                      Biology (HGF-SystemsBiology) / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017) /
                      W2Morrison - W2/W3 Professorinnen Programm der
                      Helmholtzgemeinschaft (B1175.01.12) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF2-331 / G:(DE-Juel1)HGF-SystemsBiology /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(DE-HGF)B1175.01.12 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:23316158},
      UT           = {WOS:000209207200001},
      doi          = {10.3389/fninf.2012.00031},
      url          = {https://juser.fz-juelich.de/record/128607},
}