Basili et al. , together with characterizing the effort distribution of maintenance releases, describe a simple regression model to estimate the effort needed to maintain and deliver a release. The model uses a single variable, LOC, which was measured as the sum of added, modified and deleted LOC including comments and blanks. The prediction accuracy was not reported although the coefficient of determination was relative high (R2 = 0.75), indicating that LOC is an important predictor of the maintenance effort. Jorgensen evaluated eleven different models to estimate the effort of individual maintenance tasks using regression, neural networks, and pattern recognition approaches .
The models use the size of maintenance tasks, which is also measured as the sum of added, updated, and deleted LOC, as the main size input. The best model could generate effort estimates within 25 percent of the actuals 26 percent of the time, and the mean of relative error (MMRE) is 100 percent.
Several previous studies have proposed and evaluated models exclusively for estimating the effort required to implement corrective maintenance tasks. Lucia et al. used the multiple linear regression to build effort estimation models for corrective maintenance projects . Three models were built using coarse-grained metrics, namely the number of tasks requiring source code modification (NA), the number of tasks requiring fixing of data misalignment (NB), the number of other tasks (NC), the total number of tasks, and LOC of the system to be maintained. They evaluated the models on 144 observations, each
corresponding to one-month period, collected from five corrective maintenance projects in the same software services company.
The best model, which includes all metrics, achieved effort estimates within 25 percent of the actuals 49.31 percent of the time and MMRE of 32.25%. When comparing with the non-linear model previously used by the same company, they suggested that the linear model that uses the same variables produces higher estimation accuracies. They also showed that taking into account the difference in types of corrective maintenance tasks can improve the performance of the estimation model.