Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Yasunari Takagi,Tohru Kikuno
In typical software development, a software reliability growth model
(SRGM) is applied in each testing activity to determine the time to
finish the testing.
However, there are some cases in which the SRGM does not work
correctly. That is, the SRGM sometimes mistakes quality for poor
quality products. In order to tackle this problem, we apply time
series data collected from development to quality estimation.
First, we investigate the characteristics of the time series data on
the detected faults by observing the change of the number of
detected faults. Using the rank correlation coefficient, the data
are classified into four kinds of trends. Next, with the intention
of estimating software quality, we investigate the relationship
between the trends of the time series data and software
quality. Here, software quality is defined by the number of faults
detected during six months after shipment.
Finally, we find a relationship between the trends and metrics data
collected in the software design phase. Using logistic regression,
we statistically show that two review metrics in the design \&
coding phase can determine the trend.
Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Yasunari Takagi,Tohru Kikuno
Sousuke Amasaki,Takashi Yoshitomi,Osamu Mizuno,Yasunari Takagi,Tohru Kikuno
434
0.529 (2005)
Software Quality Journal
6
2
177-193
0
A New Challenge for Applying Time Series Metrics Data to Software Quality Estimation
http://www.springerlink.com/content/l344q05u8681m7q2/
13
2005