An empirical study of the bad smells and class error probability in the post-release object-oriented system evolution

  • Authors:

  • Wei Li;Raed Shatnawi

  • Affiliations:

  • Computer Science Department, The University of Alabama in Huntsville, Huntsville, AL 35899, United States;Computer Science Department, The University of Alabama in Huntsville, Huntsville, AL 35899, United States

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2007

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Abstract

Bad smells are used as a means to identify problematic classes in object-oriented systems for refactoring. The belief that the bad smells are linked with problematic classes is largely based on previous metric research results. Although there is a plethora of empirical studies linking software metrics to errors and error proneness of classes in object-oriented systems, the link between the bad smells and class error probability in the evolution of object-oriented systems after the systems are released has not been explored. There has been no empirical evidence linking the bad smells with class error probability so far. This paper presents the results from an empirical study that investigated the relationship between the bad smells and class error probability in three error-severity levels in an industrial-strength open source system. Our research, which was conducted in the context of the post-release system evolution process, showed that some bad smells were positively associated with the class error probability in the three error-severity levels. This finding supports the use of bad smells as a systematic method to identify and refactor problematic classes in this specific context.