A Feature Envy Detection Method Based on Dataflow Analysis
| Autor: | Chien-Hung Liu, Bo-Hong Li, Woei-Kae Chen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statement (computer science)
Class (computer programming) 050208 finance Computer science business.industry Dataflow 05 social sciences Feature extraction TheoryofComputation_GENERAL 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Software Feature (computer vision) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer |
| Zdroj: | COMPSAC (2) |
| DOI: | 10.1109/compsac.2018.10196 |
| Popis: | Feature Envy is a code smell indicating that a particular class is showing too much interest in the methods/attributes of another class. Several feature-envy detection approaches have been proposed. However, these approaches consider an entire method as a unit for detection. When a method is lengthy, the exact location of the problematic statement may not be immediately obvious, and when a method mixes several kinds of behaviors, these approaches could be easily fooled. This paper proposes a characterization of feature envy and a detection approach. A tool, called FEED (FEature Envy Detector), based on dataflow analysis is developed to perform feature-envy detection. In comparison to previous approaches, the proposed approach offers a better detection granularity and also provides a better detection accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|