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Increasing quality and managing complexity in neuroinformatics software development with continuous integration.

Zaytsev YV, Morrison A - Front Neuroinform (2013)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research Center Jülich, Germany ; Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Research Center, Jülich Aachen Research Alliance Jülich, Germany ; Faculty of Biology, Albert-Ludwig University of Freiburg Freiburg im Breisgau, Germany.

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.

No MeSH data available.


Related in: MedlinePlus

Matrix build set up for the main branch of NEST. Every time a change is committed to the repository, all major configurations are built, installed and tested to catch regressions in the functionality in a timely manner.
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Figure 5: Matrix build set up for the main branch of NEST. Every time a change is committed to the repository, all major configurations are built, installed and tested to catch regressions in the functionality in a timely manner.

Mentions: This is where the matrix builds come in: the administrator only needs to define the “axes” of parameters by entering all possible values thereof, or by tagging groups of slaves with particular labels. Examples of parameter axes might include build options, such as “--with-mpi” or “--without-mpi,” or the architectures or operating systems of the build slaves. Using this information, Jenkins will generate all possible combinations of parameters and intelligently schedule builds to optimize the utilization of the slaves. Build results are presented in a summary table as illustrated on Figure 5 which gives easy access to the detailed information about each individual build.


Increasing quality and managing complexity in neuroinformatics software development with continuous integration.

Zaytsev YV, Morrison A - Front Neuroinform (2013)

Matrix build set up for the main branch of NEST. Every time a change is committed to the repository, all major configurations are built, installed and tested to catch regressions in the functionality in a timely manner.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3539731&req=5

Figure 5: Matrix build set up for the main branch of NEST. Every time a change is committed to the repository, all major configurations are built, installed and tested to catch regressions in the functionality in a timely manner.
Mentions: This is where the matrix builds come in: the administrator only needs to define the “axes” of parameters by entering all possible values thereof, or by tagging groups of slaves with particular labels. Examples of parameter axes might include build options, such as “--with-mpi” or “--without-mpi,” or the architectures or operating systems of the build slaves. Using this information, Jenkins will generate all possible combinations of parameters and intelligently schedule builds to optimize the utilization of the slaves. Build results are presented in a summary table as illustrated on Figure 5 which gives easy access to the detailed information about each individual build.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research Center Jülich, Germany ; Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Research Center, Jülich Aachen Research Alliance Jülich, Germany ; Faculty of Biology, Albert-Ludwig University of Freiburg Freiburg im Breisgau, Germany.

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.

No MeSH data available.


Related in: MedlinePlus