The need to develop quality software products “within cost and time limitations” continues to be a high priority in software development and it invariably mandates the use of metrics for resource and time estimations. Much progress has been made in this respect, but losses are still enormous.
In the U. S. alone, recent statistics indicate approximately $59 billion in cost overruns on software projects annually, and an additional $81 billion on canceled software projects annually.
The Taguchi philosophy measures the quality of a product as the “loss imparted to society” by the product after delivery of the product to the user. While developed originally in a manufacturing environment, the philosophy does not preclude software products.
It is therefore surprising that there has been limited application of the Taguchi philosophy. This is more so when one considers the fact that the related statistical concept of six-sigma analysis has been extensively applied to software products in recent years.
This paper adapts the Taguchi larger-the-best loss function to software quality metrics like Mean Time Between Failure (MTBF). Software metrics like MTBF have a desired (target) value of infinity and therefore should be as large as possible.
A value of infinity however is not easy to work with, and poses several challenges. An elegant solution is proposed in this paper. The solution uses the reciprocal of infinity (that is, zero) and the applicable Taguchi loss functions are modified to use reciprocal parameters.
Considering the many loss functions (over 68) already developed by Taguchi, this approach to quantifying software quality holds much promise for continuing and future improvements to software quality.
To read this external content in full, download the paper from the article archives at the Unversity of Michigan, Dearborn.