Exploring the Department of Defense Software Factbook

Abstract

The Carnegie Mellon Software Engineering Institute (SEI) conducted an analysis of software engineering data owned and maintained by the Department of Defense (DoD) to produce high-level, DoD-wide heuristics and domain-specific benchmark data. This work yielded basic facts about software projects, such as averages, ranges, and heuristics for requirements, size, effort, and duration. Factual, quantitatively derived statements were reported to provide users with easily digestible benchmarks. Findings were also presented by system type, or super domain. The analysis in this area focused on identifying the most and least expensive projects and the best and worst projects within three super domains: real time, engineering, and automated information systems. It also provided insight into the differences between system domains and contains domain-specific heuristics. Finally, correlations were explored among requirements, size, duration, and effort and the strongest models for predicting change were described. The goal of this work was to determine how well the data could be used to answer common questions related to planning or replanning software projects. The paper provides a high-level overview of the SEIs research and primary findings.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2018
Accession Number
AD1087445

Entities

People

  • Christopher J. Miller
  • David Zubrow
  • Forrest J. Shull

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Contracts
  • Data Analysis
  • Department Of Defense
  • Engineering
  • Governments
  • Information Systems
  • Intervals
  • Materials
  • Production
  • Production Rate
  • Productivity
  • Software Development
  • Software Metrics
  • Statistical Analysis
  • System Software
  • Universities

Fields of Study

  • Computer science
  • Engineering

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Software Engineering.