By Topic

Ranking software engineering measures related to reliability using expert opinion

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Li, M. ; Maryland Univ., College Park, MD, USA ; Smidts, C. ; Brill, R.W.

The field of software engineering measurement appears to the unfamiliar eye as a chaotic environment lacking unifying principles and rigor. The number of software engineering measures developed over the years is stupefying and keeps increasing. Software engineering measures relate to multiple aspects of the software development process and product. Software development organizations typically select a small number of such software engineering measures to manage their development processes and products. The research presented in this paper is an attempt to help software development organizations identify the software engineering measures that are best predictors of software reliability. The research is based on the top 30 measures identified in an earlier study carried out by Lawrence Livermore National Laboratory (J.D. Lawrence et al., Technical Report UCRL-ID-136035, 1998). The set of ranking criteria was modified to fit the needs of the study. The score of each measure for each ranking criterion was elicited through expert opinion and then aggregated into a single score using multi-attribute utility theory. The basic aggregation scheme selected was a linear additive scheme. A comprehensive sensitivity analysis was carried out. The sensitivity analysis included variation of levels, variation of weights and variation of aggregation schemes

Published in:

Software Reliability Engineering, 2000. ISSRE 2000. Proceedings. 11th International Symposium on

Date of Conference: