Assessing effort estimation models for corrective maintenance through empirical studies

We present an empirical assessment and improvement of the effort estimation model for corrective maintenance adopted in a major international software enterprise. Our study was composed of two phases. In the first phase we used multiple linear regression analysis to construct effort estimation models validated against real data collected from five corrective maintenance projects. The model previously adopted by the subject company used as predictors the size of the system being maintained and the number of maintenance tasks. While this model was not linear, we show that a linear model including the same variables achieved better performances. Also we show that greater improvements in the model performances can be achieved if the types of the different maintenance tasks is taken into account. In the second phase we performed a replicated assessment of the effort prediction models built in the previous phase on a new corrective maintenance project conducted by the subject company on a software system of the same type as the systems of the previous maintenance projects. The data available for the new project were finer grained, according to the indications devised in the first study. This allowed to improve the confidence in our previous empirical analysis by confirming most of the hypotheses made. The new data also provided other useful indications to better understand the maintenance process of the company in a quantitative way.