Analyzing Code Smell Removal Sequences for Enhanced Software Maintainability

Code smells are the surface indications which affect the maintainability of the software. Code smells disturb the maintainability of the code by starting a chain reaction of breakages in dependent modules which makes it difficult to read and modify. Applying appropriate refactoring sequences by prioritizing the classes to obtain maintainable software is a tedious process due to strict deadlines of the projects for the developers. Recent researches have explored varied ways of ranking the classes to improve the maintainability. This work empirically investigates the impact of eliminating three prominent code smells by considering their six possible combinations. Our work prioritizes the object oriented software classes in the code that are in the need of refactoring. For prioritizing the refactoring prone classes, a proposed metric, maintainability complexity index is calculated using the values of maintainability index and relative logical complexity as the inputs. The study outcomes show the values of maintainability predicting metrics for the corresponding permutation of the code smell removal sequence. Also, the work aims to yield the sequence which gives software with maximum maintainability so that developers and researchers can save their effort and time to produce high quality software.