Eugenio Pompella

Assessing effort estimation models for corrective maintenance through empirical studies

Abstract 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.

Assessing the maintenance processes of a software organization: an empirical analysis of a large industrial project

The use of statistical process control methods can determine the process capability of sustaining stable levels of variability, so that processes will yield predictable results. This enables to prepare achievable plans, meet cost estimates and scheduling commitments, and deliver required product functionality and quality with acceptable and reasonable reliability. We present initial results of applying statistical analysis methods to the maintenance processes of a software organization rated at the CMM level 3 that is currently planning the assessment to move to the CMM level 4. In particular, we present results from an empirical study conducted on the massive adaptive maintenance process of the organization. We analyzed the correlation between the maintenance size and productivity metrics. The resulting models allow to estimate the costs of a project conducted according to the adopted maintenance processes. Model performances on future observations were assessed by means of a cross validation which guarantees a nearly unbiased estimate of the prediction error. Data about the single phases of the process were also available, thus allowing to analyze the distribution of the effort among the phases and the causes of variations.

Effort estimation for corrective software maintenance

This paper reports on an empirical study aiming at constructing cost estimation models for corrective maintenance projects. Data available were collected from five maintenance projects currently carried out by a large software enterprise. The resulting models, constructed using multivariate linear regression techniques, allow to estimate the costs of a project conducted according to the adopted maintenance processes. Model performances on future observations were achieved by taking into account different corrective maintenance task typologies, each affecting the effort in a different way, and assessed by means of a cross validation which guarantees a nearly unbiased estimate of the prediction error. The constructed models are currently adopted by the subject company.

Assessing massive maintenance processes: an empirical study

We present an empirical study from the experience of a major. international software enterprise in conducting massive adaptive maintenance projects with a close deadline. The adopted process entails the decomposition of the application portfolio into loosely coupled work-packets that can be independently and incrementally worked out by teams distributed on different sites. The study analyzes the correlation between maintenance size and productivity metrics of a large Y2K project. The resulting models allows to estimate the costs of a project conducted according to the adopted massive maintenance process and distribute them among the different phases.

Empirical analysis of massive maintenance processes

We present initial results of applying statistical control techniques to the massive maintenance processes of a software organization rated at the CMM level 3. In particular, we present results from an empirical study conducted on a large massive adaptive maintenance project. In a previous study (2001) we analyzed the correlation between the maintenance size and productivity metrics and produced models to estimate the costs of a project conducted according to the adopted maintenance processes. In this paper we analyze data about the single phases of the process and, in particular, the distribution of the effort among the phases and causes of variations.

Assessing Effort Prediction Models for Corrective Software Maintenance

We present an assessment of an empirical study aiming at building effort estimation models for corrective maintenance projects. We show results from the application of the prediction models to a new corrective maintenance project within the same enterprise and the same type of software systems used in a previous study. The data available for the new project are 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 and to provide other useful indications to better understand the maintenance process of the company in a quantitative way.

Applying workflow management to support massive maintenance

Workflow management systems have proven useful for improving the management end execution of processes in several application domains, including software engineering. In this paper we discuss issues and preliminary results of a project aiming at introducing workflow technology in a software maintenance organization. We apply a four steps process to model the workflows, and the associated flows of documentations of the massive maintenance process. The paper describes the models obtained and discusses how they have been implemented using market-widespread workflow technology.