Abstract
The purpose of this paper is to provide the software engineer with tools from the field of manufacturing as an aid to improving software process and product quality. Process involves classical manufacturing methods, such as statistical quality control applied to product testing, which is designed to monitor and correct the process when the process yields product quality that fails to meet specifications. Product quality is measured by metrics, such as failure count occurring on software during testing. When the process and product quality are out of control, we show what remedial action to take to bring both the process and product under control. NASA Space Shuttle failure data are used to illustrate the process methods.
Highlights
Based on “old” ideas from the field of manufacturing, we propose “new” ideas for software practitioners to consider for controlling the quality of software
We have presented some process methods from the field of manufacturing with the objective that the methods will prove useful to software engineers
The loss function and signal to noise ratio inspired by Taguchi methods proved to be valuable techniques for identifying and reducing excessive variation in software quality
Summary
Based on “old” ideas from the field of manufacturing, we propose “new” ideas for software practitioners to consider for controlling the quality of software. There are obviously significant difference between hardware and software, there are design quality control processes that can be adapted from hardware and applied advantageously to software. Among these are the Taguchi manufacturing methods, statistical quality control charts (statistical quality control is the use of statistical methods to control quality [1]), and design of experiments. You can control the inherent variation by identifying and controlling its cause, thereby bringing the process under statistical control [2]. We explore methods, like Taguchi methods, that tie process to product We apply these methods to NASA Space Shuttle software using actual failure data
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