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Ontology for Software Production: Insights and Methodologies for Effective Decision Making

This document by James Kirby, Jr., supported by contributors like David Weiss and Robyn Lutz, presents a comprehensive ontology for software production. It explores the complexities of software creation, sustainment, and assurance over years of development and operation. Key topics include decision-making processes, organization and recording of decisions, and capturing these in an ontology. The study highlights the challenges of inadequate understanding and the absence of standardization across various industrial settings, such as avionics, automotive, and telecommunications.

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Ontology for Software Production: Insights and Methodologies for Effective Decision Making

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  1. Ontology for Software Production James Kirby, Jr.* Naval Research Laboratory james.kirby@nrl.navy.mil *With contributions from David Weiss, Robyn Lutz, and others

  2. Software Production • Software creation + sustainment (evolution) + assurance • Years of development • Decades of operation • Decision making process • What are those decisions? • How are decisions recorded? • How are decisions organized? • Capture decisions in ontology • Instantiating the ontology describes design of a particular system

  3. Software Production Ontology

  4. Understanding Software Production • Inadequate, anecdotal understanding • How to create, sustain software infrastructure in response to changing needs and technologies? • Few examples of large-scale data collection • Little standardization of data • Widely-varying industrial settings • Avionics, Automotive, Medical, Energy, Defense, Telecommunications, Entertainment • Evidence is often anecdotal or based on small- scale trials

  5. Software Production Data • Decisions considered, rejected, made, changed • Rationale • Formal software artifacts • Source and executable code; specifications; machine-readable models • Structured informal artifacts • Pseudo-code, requirements, graphical models, test plans, email addressing info, subject • Unstructured artifacts • Email body, notes, code comments, etc.

  6. Software Production • Information process • Sequence of representations (Denning) • Software production representation • Union of existing artifacts at point in time • Sequence of representations • Demarcated by creation of artifacts and changes to them over time • State space of software production

  7. Software Production Information Process • Open source software (OSS) Mozilla Core as of 20 May 2011 • 1379 developers over 13 years • 10.8M lines of codes and comments • 205,799 changes committed to version control system • State space of Mozilla Core software production • 205,799 representations X average of ~5M lines • Note: • Artifacts other than code not considered • Not a very large project • One OSS project vs. Commercial vs. contractor-developed government project vs. .....

  8. Questions?

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