A New Challenge for Applying Time Series Metrics Data to Software Quality Estimation

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 focussed on the trend of time series data of software defects among successive testing phases and tried to estimate software quality using the trend. 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 and coding phase can determine the trend.