NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Learning from examples - Generation and evaluation of decision trees for software resource analysisA general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.
Document ID
19890033873
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Selby, Richard W.
(California Univ. Irvine, CA, United States)
Porter, Adam A.
(California, University Irvine, United States)
Date Acquired
August 14, 2013
Publication Date
December 1, 1988
Publication Information
Publication: IEEE Transactions on Software Engineering
Volume: 14
ISSN: 0098-5589
Subject Category
Computer Programming And Software
Accession Number
89A21244
Funding Number(s)
CONTRACT_GRANT: NSG-5123
CONTRACT_GRANT: NSF CCR-87-04311
CONTRACT_GRANT: NSF DCR-85-21398
Distribution Limits
Public
Copyright
Other

Available Downloads

There are no available downloads for this record.
No Preview Available