Quantitative analysis of faults and failures in a complex software system

Abstract

The authors describe a number of results from a quantitative study of faults and failures in two releases of a major commercial software system. They tested a range of basic software engineering hypotheses relating to: the Pareto principle of distribution of faults and failures; the use of early fault data to predict later fault and failure data; metrics for fault prediction; and benchmarking fault data. For example, we found strong evidence that a small number of modules contain most of the faults discovered in prerelease testing and that a very small number of modules contain most of the faults discovered in operation. We found no evidence to support previous claims relating module size to fault density nor did we find evidence that popular complexity metrics are good predictors of either fault-prone or failure-prone modules. We confirmed that the number of faults discovered in prerelease testing is an order of magnitude greater than the number discovered in 12 months of operational use. The most important result was strong evidence of a counter-intuitive relationship between pre- and postrelease faults; those modules which are the most fault-prone prerelease are among the least fault-prone postrelease, while conversely, the modules which are most fault-prone postrelease are among the least fault-prone prerelease. This observation has serious ramifications for the commonly used fault density measure. Our results provide data-points in building up an empirical picture of the software development process.