Integration of systems engineering best practices with DOD acquisition practices

AbstractComputational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models are typically simulated, calibrated, and analyzed either within a single application, or using import and export from various tools. Here, we describe a programmatic modeling paradigm, in which modeling is augmented with best practices from software engineering. We focus on Python - a popular, user-friendly programming language with a large scientific package ecosystem. Models themselves can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators while still being exportable to SBML. Automated version control and testing ensures models and their modules have expected properties and behavior. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility.HighlightsProgrammatic modeling combines computational modeling with software engineering best practices.An executable model enables users to leverage all available resources from the language.Community benefits include improved collaboration, reusability, and reproducibility.Python has multiple modeling frameworks with a broad, active scientific ecosystem.

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Software and Systems Engineering

Practicing Software Engineering in the 21st Century ◽

10.4018/978-1-93177-750-6.ch003 ◽

2003 ◽

pp. 26-41

Author(s):

Rick Gibson

Keyword(s):

Software Engineering ◽

Best Practices ◽

Systems Engineering ◽

Capability Maturity Model ◽

Maturity Model ◽

Organizational Cultures ◽

Capability Maturity ◽

Key Aspects ◽

Significant Point

This chapter will identify the key aspects of software engineering and systems engineering in an effort to highlight areas of consensus and conflict to support current efforts by practitioners and academics in both disciplines in redefining their professions and bodies of knowledge. By using the Software Engineering Institute’s Capability Maturity Model –Integrated (CMMISM) project, which combines best practices from the systems and software engineering disciplines, it can be shown that significant point of agreement and consensus are evident. Nevertheless, valid objections to such integration remain as areas of conflict. This chapter will provide an opportunity for these two communities to resolve unnecessary differences in terminology and methodologies that are reflected in their different perspectives and entrenched in their organizational cultures.

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