Causal Models for Software Cost Prediction and Control (SCOPE)

reportActive / Technical Report | Accesssion Number: AD1110368 | Open PDF

Abstract:

Why Causal Learning? Estimating and controlling program costs benefits from causal knowledge of program dynamics. Regression does not distinguish between correlation and causation. Causal knowledge is actionable knowledge. Causal discovery is becoming practical and is supported with innovative tools and algorithms.

Author(s):
Konrad, Mike ; Stoddard, Robert ; Nichols, Bill ; Zubrow, Dave ; Falce, Michele ; Brown, Rhonda ; Wassum, Bryar
Author Organization(s):
CARNEGIE-MELLON UNIV PITTSBURGH PA
Funding Organization(s):
Publication Date:
2020 Jan 01
Pagination:
0

Security Markings

RECORD

Collection: TRECMS
Subject Terms
Modernization Areas:
Cyber
Communities of Interest:
No COI(s) Identified
Descriptor(s):
software development, data sets, department of defense, engineering, reliability, systems engineering, causal reasoning, statistics, algorithms, artifacts, cost estimates, costs, lessons learned, materials, sustainment, transitions, universities
Keyword(s):
Causal Learning, OBSERVATIONAL DATA, software cost
Subject Categories:
Mathematical and Computer Sciences; Mathematical and Computer Sciences
Update Date:
2020 Oct 05