SOFTWARE DESIGN FOR X - Software Design for Six-Sigma: A Roadmap for Excellence - page 373

Coupling between objects
Response for a class
Number of child classes
Depth of inheritance tree
B. Dynamic Metrics : The dynamic metric has two major measurements: failure
rate data and problem reports. The goal of collecting fault and failure metrics
is to be able to determine when the software is approaching failure-free exe-
cution. Minimally, both the number of faults found during testing (i.e., before
delivery) and the failures (or other problems) reported by users after delivery
are collected, summarized, and analyzed to achieve this goal. Test strategy
is highly relative to the effectiveness of fault metrics because if the testing
scenario does not cover the full functionality of the software, then the software
may pass all tests and yet be prone to failure once delivered. Usually, failure
metrics are based on customer information regarding failures found after re-
lease of the software. The failure data collected, therefore, is used to calculate
failure density, mean time between failures (MTBF), or other parameters to
measure or predict software reliability.

14.2.4 DFR in Software DFSS

In the context of DFR, we found the following practices are dominating the software
industry, in particular for software products containing embedded code:

Quality through testing : Quality through software testing is the most prevalent
approach for implementing software reliability within small or unstructured de-
velopment companies. This approach assumes that reliability can be increased by
expanding the types of system tests (e.g., integration, performance, and loading)
and increasing the duration of testing. Software reliability is measured by vari-
ous methods of defect counting and classification. Generally, these approaches
fail to achieve their software reliability targets.
Traditional reliability programs : Traditional software reliability programs treat
the development process as a software-generating black box. Predictive models
are generated, usually by a separate team of reliability engineers, to provide
estimates of the number of faults in the resulting software; greater consistency
in reliability leads to increased accuracy in the output modeling. Within the
black box, a combination of reliability techniques like failure analysis, (e.g.,
Failure Mode and Effects Analysis [FMEAs], Fault Tree Analysis [FTAs], defect
tracking, and operational profile testing) are used to identify defects and produce
software reliability metrics.
Process control : Process control assumes a correlation between process ma-
turity and latent defect density in the final software. Companies implement-
ing Capability Maturity Model (CMM) Level 3 processes generate software