Journal
JOURNAL OF SYSTEMS AND SOFTWARE
Volume 161, Issue -, Pages -Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jss.2019.110486
Keywords
Software quality; Anti-patterns; Machine learning; Ensemble methods
Funding
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Canada Research Chair on Patterns in Mixed-language Systems
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Anti-patterns are poor solutions to recurring design problems. Several empirical studies have highlighted their negative impact on program comprehension, maintainability, as well as fault-proneness. A variety of detection approaches have been proposed to identify their occurrences in source code. However, these approaches can identify only a subset of the occurrences and report large numbers of false positives and misses. Furthermore, a low agreement is generally observed among different approaches. Recent studies have shown the potential of machine-learning models to improve this situation. However, such algorithms require large sets of manually-produced training-data, which often limits their application in practice. In this paper, we present SMAD (SMart Aggregation of Anti-patterns Detectors), a machine-learning based ensemble method to aggregate various anti-patterns detection approaches on the basis of their internal detection rules. Thus, our method uses several detection tools to produce an improved prediction from a reasonable number of training examples. We implemented SMAD for the detection of two well known anti-patterns: God Class and Feature Envy. With the results of our experiments conducted on eight java projects, we show that: (1) Our method clearly improves the so aggregated tools; (2) SMAD significantly outperforms other ensemble methods. (C) 2019 Elsevier Inc. All rights reserved.
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