Study on the failure intensity of different software faults

Kazuyuki Shima; Shingo Takada; Ken'ichi Matsumoto; Koji Torii

Study on the failure intensity of different software faults

Kazuyuki Shima, Shingo Takada, Ken'ichi Matsumoto, Koji Torii

研究成果: Conference article › 査読

抄録

We describe an experiment investigating the distribution of failure intensity in software reliability growth models. We found that the assumption of conventional models that the failure intensity follows a gamma distribution is not always true. Our new software reliability model does not make this assumption; rather, the failure intensity is calculated from failure data. We show that our new model predicts more accurately the number of detected faults for our study project than the conventional models.

本文言語	English
ページ（範囲）	86-94
ページ数	9
ジャーナル	Proceedings - International Conference on Software Engineering
出版ステータス	Published - 1997 1 1
外部発表	はい
イベント	Proceedings of the 1997 IEEE 19th International Conference on Software Engineering - Boston, MA, USA 継続期間: 1997 5 17 → 1997 5 23

ASJC Scopus subject areas

Software

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引用スタイル

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abstract = "We describe an experiment investigating the distribution of failure intensity in software reliability growth models. We found that the assumption of conventional models that the failure intensity follows a gamma distribution is not always true. Our new software reliability model does not make this assumption; rather, the failure intensity is calculated from failure data. We show that our new model predicts more accurately the number of detected faults for our study project than the conventional models.",

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AB - We describe an experiment investigating the distribution of failure intensity in software reliability growth models. We found that the assumption of conventional models that the failure intensity follows a gamma distribution is not always true. Our new software reliability model does not make this assumption; rather, the failure intensity is calculated from failure data. We show that our new model predicts more accurately the number of detected faults for our study project than the conventional models.

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