Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Handbook of software reliability engineering
Handbook of software reliability engineering
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Using a Proportional Hazards Model to Analyze Software Reliability
STEP '99 Proceedings of the Software Technology and Engineering Practice
Software Reliability Corroboration
SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
A Ranking of Software Engineering Measures Based on Expert Opinion
IEEE Transactions on Software Engineering
Evidential volume approach for certification
Ada-Europe'03 Proceedings of the 8th Ada-Europe international conference on Reliable software technologies
| Hi-index | 0.00 |
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 current research is based on the top thirty measures identified in an earlier study carried out by Lawrence Livermore National Laboratory. 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.