Quantitative Analysis of Faults and Failures in a Complex Software System

Quantitative Analysis of Faults and Failures in a Complex Software System (2000)

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by Norman E Fenton , Niclas Ohlsson

Venue:	IEEE Transactions on Software Engineering
Citations:	111 - 5 self

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Datum	Value	Source
TITLE	Quantitative Analysis of Faults and Failures in a Complex Software System	INFERENCE
AUTHOR NAME	Norman E Fenton	SVM HeaderParse 0.2
AUTHOR AFFIL	Centre for Software Reliability Dept Computer and Information Sciences; City University	SVM HeaderParse 0.2
AUTHOR ADDR	University of Linköping; Northampton Square, London EC1V OHB S-581 83 Linköping; UK Sweden	SVM HeaderParse 0.2
AUTHOR NAME	Niclas Ohlsson	SVM HeaderParse 0.2
AUTHOR AFFIL	Centre for Software Reliability Dept Computer and Information Sciences; City University	SVM HeaderParse 0.2
AUTHOR ADDR	University of Linköping; Northampton Square, London EC1V OHB S-581 83 Linköping; UK Sweden	SVM HeaderParse 0.2
ABSTRACT	The dearth of published empirical data on major industrial systems has been one of the reasons that software engineering has failed to establish a proper scientific basis. In this paper we hope to provide a small contribution to the body of empirical knowledge. We describe a number of results from a quantitative study of faults and failures in two releases of a major commercial system. We 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 pre-release testing, and that a very small number of modules contain most of the faults discovered in operation. However, in neither case is this explained by the size or complexity of the modules. 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 pre-release testing is an order of magnitude greater than the number discovered in 12 months of operational use. We also discovered fairly	SVM HeaderParse 0.2
YEAR	2000	INFERENCE
VENUE	IEEE Transactions on Software Engineering	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	797--814	INFERENCE
VOLUME	26	INFERENCE
CITATIONS	40 found	ParsCit 1.0