Detecting code smells using machine learning techniques: Are we there yet?
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Cites background from "Detecting code smells using machine..."
...As shown in a recent work [97], the dataset might influence the performance of machine learning models....
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Cites background or result from "Detecting code smells using machine..."
...by previous work [34], the composition of the dataset might influence the performance of a technique; this is especially true in the case of code smell detection, where a detector should recognize code smells over datasets that are both unbalanced (i....
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...Although the use of machine learning looks promising, its actual accuracy for code smell detection is still under debate, as previous work has observed contrasting results [32], [34]....
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...[34] demonstrated that, in a real use-case scenario, the results achieved by Arcelli Fontana et al....
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64 citations
Cites background from "Detecting code smells using machine..."
...Such machine learning based approaches have proved to be effective and efficient although some experimental evaluation also reveals their significant limitations [41]....
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...However, empirical studies [41] suggest that such statistical machine learning based smell detection...
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References
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79,257 citations
"Detecting code smells using machine..." refers methods in this paper
...[1] evaluated six basic ML techniques: J48 [61], JRIP [62], RANDOM FOREST [63], NAIVE BAYES [64], SMO [65], and LIBSVM [66]....
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...The results achieved by Arcelli Fontana et al. [1] reported that most of the classifiers have accuracy and F-Measure higher than 95%, with J48 and RANDOM FOREST being the most powerful ML techniques....
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...The best performance (for all the smells) is achieved by the tree-based classifiers, i.e., RANDOM FOREST and J48: this confirms the results of the reference study, which highlighted how this type of classifiers perform better than the others....
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...In their study, they found that all the machine learners experimented achieved high performance in a cross-project scenario, with the J48 and RANDOM FOREST classifiers obtaining the highest accuracy....
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...Arcelli Fontana et al. [1] evaluated six basic ML techniques: J48 [61], JRIP [62], RANDOM FOREST [63], NAIVE BAYES [64], SMO [65], and LIBSVM [66]....
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40,826 citations
"Detecting code smells using machine..." refers methods in this paper
...[1] evaluated six basic ML techniques: J48 [61], JRIP [62], RANDOM FOREST [63], NAIVE BAYES [64], SMO [65], and LIBSVM [66]....
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...As for J48, the three types of pruning techniques available in WEKA [67] were used, SMO was based on two kernels (e.g., POLYNOMIAL and RBF), while for LIBSVM eight different configurations, using C-SVC and V-SVC, were used....
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...Arcelli Fontana et al. [1] evaluated six basic ML techniques: J48 [61], JRIP [62], RANDOM FOREST [63], NAIVE BAYES [64], SMO [65], and LIBSVM [66]....
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21,674 citations
19,603 citations
"Detecting code smells using machine..." refers methods in this paper
...As for J48, the three types of pruning techniques available in WEKA [67] were used, SMO was based on two kernels (e.g., POLYNOMIAL and RBF), while for LIBSVM eight different configurations, using C-SVC and V-SVC, were used....
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...As for the experimented prediction models, we exploited the implementation provided by the WEKA framework [67], which is widely considered as a reliable tool....
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...As for J48, the three types of pruning techniques available in WEKA [67] were used, SMO was based on two kernels (e....
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17,313 citations