Influence of Programming Style in Transformation Bad Smells: Mining of ETL Repositories

Bad smells affect maintainability and performance of model-to-model transformations. A number of studies have defined a set of transformation bad smells, and proposed techniques to recognize and —according to their complexity— fix them in a (semi-)automated way. In education, it is necessary to make students aware of this subject and provide them with guidelines to improve the quality of their transformations. This paper presents some common bad smells in model transformations written by master students from Universidad de los Andes and compares them with that of publicly available repositories of ETL transformations, for the purpose of knowing whether programming style affects the incidence of smells. Three contributions are presented: i) Two new bad smell patterns enriching the existing catalogs; ii) A process that includes the automated extraction of transformation metrics and bad smells metrics from the repositories, and a statistical analysis that helps in identifying the relations between such metrics; and iii) A tool that supports the process. By applying our approach on the datasets, we discuss whether it is easier for students with imperative programming language background to make use of appropriate declarative constructs of a transformation language compared to imperative ones. We conclude that students must be encouraged and guided to use declarative constructs whereas possible when developing declarative transformations, that results in artifacts that are more maintainable and with a better performance.