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@INPROCEEDINGS{Feld:892906,
      author       = {Feld, Christian and Geimer, Markus and Hermanns,
                      Marc-André and Saviankou, Pavel and Visser, Anke and Mohr,
                      Bernd},
      title        = {{D}etecting {D}isaster {B}efore {I}t {S}trikes: {O}n the
                      {C}hallenges of {A}utomated {B}uilding and {T}esting in
                      {HPC} {E}nvironments},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2021-02430},
      isbn         = {978-3-030-66057-4},
      pages        = {3-26},
      year         = {2021},
      comment      = {Tools for High Performance Computing 2018 / 2019 / Mix,
                      Hartmut (Editor) ; Cham : Springer International Publishing,
                      2021, Chapter 1 ; ISBN: 978-3-030-66056-7 ;
                      doi:10.1007/978-3-030-66057-4},
      booktitle     = {Tools for High Performance Computing
                       2018 / 2019 / Mix, Hartmut (Editor) ;
                       Cham : Springer International
                       Publishing, 2021, Chapter 1 ; ISBN:
                       978-3-030-66056-7 ;
                       doi:10.1007/978-3-030-66057-4},
      abstract     = {Software reliability is one of the cornerstones of any
                      successful user experience. Software needs to build up the
                      users’ trust in its fitness for a specific purpose.
                      Software failures undermine this trust and add to user
                      frustration that will ultimately lead to a termination of
                      usage. Even beyond user expectations on the robustness of a
                      software package, today’s scientific software is more than
                      a temporary research prototype. It also forms the bedrock
                      for successful scientific research in the future. A
                      well-defined software engineering process that includes
                      automated builds and tests is a key enabler for keeping
                      software reliable in an agile scientific environment and
                      should be of vital interest for any scientific software
                      development team. While automated builds and deployment as
                      well as systematic software testing have become common
                      practice when developing software in industry, it is rarely
                      used for scientific software, including tools. Potential
                      reasons are that (1) in contrast to computer scientists,
                      domain scientists from other fields usually never get
                      exposed to such techniques during their training, (2)
                      building up the necessary infrastructures is often
                      considered overhead that distracts from the real science,
                      (3) interdisciplinary research teams are still rare, and (4)
                      high-performance computing systems and their programming
                      environments are less standardized, such that published
                      recipes can often not be applied without heavy modification.
                      In this work, we will present the various challenges we
                      encountered while setting up an automated building and
                      testing infrastructure for the Score-P, Scalasca, and Cube
                      projects. We will outline our current approaches,
                      alternatives that have been considered, and the remaining
                      open issues that still need to be addressed—to further
                      increase the software quality and thus, ultimately improve
                      user experience.},
      month         = {Sep},
      date          = {2018-09-17},
      organization  = {12th International Parallel Tools
                       Workshop, Stuttgart (Germany), 17 Sep
                       2018 - 18 Sep 2018},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Enabling Computational- $\&$ Data-Intensive Science
                      and Engineering (POF4-511) / ATMLPP - ATML Parallel
                      Performance (ATMLPP)},
      pid          = {G:(DE-HGF)POF4-511 / G:(DE-Juel-1)ATMLPP},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-030-66057-4_1},
      url          = {https://juser.fz-juelich.de/record/892906},
}