Code smells help to discover and describe deeper problems in software design. Several automated methods of smell detection are based the analysis of a combination of code-related metrics relevant for a given flaw. However, some smells reflect more complex issues and require a holistic perspective that woudl cover a number of different sources of data. In this paper we experimentally verify the usefulness of including structural factors into a metrics-based detection of God Class and Brain Class code smells.
Walter, B., Matuszyk, B., ARCELLI FONTANA, F. (2015). Including structural factors into the metrics-based code smells detection. In ACM International Conference Proceeding Series (pp.1-5). Association for Computing Machinery [10.1145/2764979.2764990].
Including structural factors into the metrics-based code smells detection
ARCELLI FONTANA, FRANCESCA
2015
Abstract
Code smells help to discover and describe deeper problems in software design. Several automated methods of smell detection are based the analysis of a combination of code-related metrics relevant for a given flaw. However, some smells reflect more complex issues and require a holistic perspective that woudl cover a number of different sources of data. In this paper we experimentally verify the usefulness of including structural factors into a metrics-based detection of God Class and Brain Class code smells.
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