A Slice-Based Estimation Approach for Maintenance Effort

Program slicing is used as a basis for an approach to estimate maintenance effort. A case study of the GNU Linux kernel with over 900 versions spanning 17 years of history is presented. For each version a system dictionary is built using a lightweight slicing approach and encodes the forward decomposition static slice profiles for all variables in all the files in the system. Changes to the system are then modeled at the behavioral level using the difference between the system dictionaries of two versions. The three different granularities of slice (i.e., line, function, and file) are analyzed. We use a direct extension of srcML to represent computed change information. The retrieved information reflects the fact that additional knowledge of the differences can be automatically derived to help maintainers understand code changes. We consider the hypotheses: (1) The structured format helps create traceability links between the changes and other software artifacts. (2) This model is predictive of maintenance effort. The results demonstrate that the approach accurately predicts effort in a scalable manner.