Pattern Recognition Methods for Determining Software Quality.

Abstract

The On-Line Pattern Analysis and Recognition System (OLPARS) was demonstrated as a tool for evaluating program characteristics which contribute to program readability, freedom from errors, and development time. Structural features were extracted automatically from a data base of 155 PL/I programs and used as inputs to OLPARS. As expected, program length had a dominant effect on the time required to uderstand programs; additional features affecting understandability included GOTO and RETURN statements. Significant contributions of this study were methods for estimating program error rates from archival data, and reliable techniques for estimating understandability of programs. An interesting by-product was the ability to identify individual programmer styles. The principal outcome of the study was the demonstration that OLPARS could provide a facility for evaluating factors contributing to software quality. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1977
Accession Number
ADA046588

Entities

People

  • Christopher Landauer
  • David A. Bennett
  • Harry M. Hersh
  • John M. Morris
  • Thomas L. Mcgibbon

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Computer Programming
  • Computer Programs
  • Computers
  • Data Mining
  • Data Science
  • Databases
  • Feature Extraction
  • Information Processing
  • Information Science
  • Language
  • Pattern Recognition
  • Procedures (Computers)
  • Regression Analysis
  • Statistical Algorithms
  • Test Sets
  • Weight

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Computer Science.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference