Statistical Software Engineering

Abstract

This report spotlights problem areas in software engineering to which the application of modern statistical methodology can be fruitfully applied. It was produced by an expert cross-disciplinary National Research Council panel. The panel deliberated, discussed, and made recommendations based upon their experience and on information gathered at a two-day public forum at which there were presentations by 12 software and industrial engineers, scientists, and statisticians. The most important findings are: What is needed to address the challenge of cost-effectively building huge high quality software systems is productive interactions between software engineers and statisticians. Essential catalysts for this interactions include: Following a collaborative model that partners statisticians, software engineers and a REAL software process or product; following a model for data collection and analysis that ensures availability of high quality data for statistical approaches to software engineering issues; and paying attention to relevant issues in education, such as in the areas of designed experiments, exploratory data analysis, modeling, risk analysis, attitudes toward assumptions, visualization, and statistical computing tools.

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Document Details

Document Type
Technical Report
Publication Date
Apr 13, 1998
Accession Number
ADA344440

Entities

People

  • John R. Tucker

Organizations

  • National Academy of Sciences

Tags

Communities of Interest

  • Biomedical
  • Engineered Resilient Systems
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Computational Science
  • Computer Programming
  • Computer Programs
  • Data Mining
  • Data Science
  • Databases
  • Industrial Engineering
  • Information Processing
  • Information Science
  • Monte Carlo Method
  • Network Science
  • Software Development
  • Software Development Tools
  • Software Metrics
  • Software Testing
  • Statistical Algorithms
  • Surveys

Fields of Study

  • Computer science
  • Engineering

Readers

  • Academic Conference Management
  • Software Engineering.
  • Theoretical Analysis.