This work was supported by National Key Research and Development Program of China (Grant No. 2018YFB1003900) and in part by National Natural Science Foundation of China (Grant Nos. 61722202, 61772107).
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Figure 1
Approach overview.
Figure 2
The correlation analysis results of Divergent Change.
Figure 4
The correlation analysis results of Parallel Inheritance.
Figure 5
The decision tree generated by Divergent Change.
Figure 7
The decision tree generated by Parallel Inheritance.
| @@extracolsepfillcp115mm@ Terminology | Description |
| CVParameterSelection | A meta-classifier for parameter optimization selection in Weka to find the optimal value for one or more parameters within a specified range. |
| Filter | A function used to manipulate attributes and instances in the data preprocessing phase. |
| ReplaceMissingValues | A function for replacing all missing values for nominal and numeric attributes in a dataset with the modes and means from the training data. |
| AttributeSelection | A supervised attribute filter that can be used to select features. |
| InfoGainAttributeEval | An evaluator that evaluates the worth of an attribute by measuring the information gain with respect to the class. |
| Ranker | A search strategy that ranks attributes by their individual evaluations. |
| ArrayEditor | A module for inputting parameters during parameter optimization. |
| BatchSize | A parameter for the preferred number of instances to process when a batch prediction is being performed. |
| DonotCheckCapabilities | A module that decides whether to check the classifier function. |
| NominalToBinaryFitter | A module that converts the nominal attribute type to a numeric attribute. |
| Ridge Estimation | A biased estimation regression method for collinear data analysis. |
| Category | Name | Definition | Category | Name | Definition |
| Basic | Address | Address of class | Complexity | AMW | Average methods weight |
| BUR | Usage ratio | AC | Attribute complexity | ||
| Class_name | Name of class | CC | Cyclomatic complexity | ||
| CRIX | Children ratio | EC | Essential complexity | ||
| GREEDY | Raised exceptions | NORM | Number of remote methods | ||
| is static | is static class | NOAV | Number of accessed variables | ||
| is leaf-class | is leaf-class | NOLV | Number of local variable | ||
| is interface | is interface | NOEU | Number of external variables | ||
| is root-class | is root-class | NOP_M | Number of parents in the method | ||
| is abstract | is abstract class | NOP_Pr | Number of parents in the project | ||
| Package | Package of class | RFC | Response for class | ||
| Project | Project of class | WOC | Weight of a class | ||
| PNAS | Public number of accessors | WMC | Weighted methods count | ||
| PS | Package size | WMPC1 | Weighted methods per class 1 | ||
| Code Size | LOC | lines of code in the class | WMPC2 | Weighted methods per class 2 | |
| LOCC | Lines of code classes | Coupling | AOFD | Access of foreign data | |
| LOC_M | lines of code in the method | ATFD | Access to foreign data | ||
| LOC_Pa | lines of code in the packages | CE | Efferent coupling | ||
| LOC_Pr | lines of code in the project | ChC | Changing classes | ||
| NOCON | Number of constructors | CM | Changing methods | ||
| NOM | Number of methods | CBO | Coupling between objects | ||
| NOM_Pr | Project number of methods | DD | Dependency dispersion | ||
| NOM_Pa | Package number of methods | DAC | Data abstraction coupling | ||
| NOA | Number of ancestors | FDP | Foreign data providers | ||
| NAM | Number of accessor methods | FANOUT | Number of classes referenced | ||
| NOD | Number of descendants | FANIN | Number of classes that reference | ||
| NOIS | Number of import statements | MIC | Method invocation coupling | ||
| NOO | Number of operations | NOEC | Number of external clients | ||
| NCC | Number of client classes | NRSS | Number of static calls | ||
| NOIC | Number of internal clients | NOED | Number of external dependencies | ||
| PProtM | Percentage of protected members | WCM | Weighted changing methods | ||
| PPubM | Percentage of public members | Cohesion | ALD | Access of local data | |
| PPrivM | Percentage of private members | AID | Access of import data | ||
| Inheritance | DIT | Depth of inheritance tree | TCC | Tight class cohesion | |
| DOIH | Depth of inheritance hierarchy | Encapsulation | LAA | Locality of attribute accesses | |
| HIT | Height of inheritance tree | NOPA | Number of public attribute | ||
| NOC | Number of children | NOAM | Number of added methods | ||
| NOCC | Number of child classes | NOOM | Number of overridden methods | ||
| NOC_Pr | Number of children in the project | Concern | CDC | Concern diffusion over components | |
| NOC_Pa | Number of children in the package | CDO | Concern diffusion over operations |
| Project | KLOC | Packages | Classes | Divergent Change | Parallel Inheritance | Shotgun Surgery |
| Aardvark | 25 | 11 | 103 | 0 | 0 | 0 |
| And Engine | 20 | 90 | 596 | 0 | 0 | 0 |
| frameworks-base | 770 | 253 | 2766 | 2 | 3 | 0 |
| cassandra | 117 | 43 | 826 | 3 | 0 | 0 |
| commons-codec | 23 | 7 | 103 | 0 | 0 | 0 |
| commons-logging | 23 | 17 | 61 | 1 | 3 | 0 |
| derby | 166 | 194 | 1746 | 0 | 0 | 0 |
| Eclipse Core | 162 | 843 | 1190 | 0 | 7 | 0 |
| Google Guava | 16 | 25 | 153 | 0 | 0 | 0 |
| HealthWatcher | 6 | 26 | 132 | 12 | 0 | 7 |
| James Mime4j | 280 | 26 | 250 | 1 | 0 | 0 |
| MobileMedia | 3 | 10 | 51 | 4 | 0 | 3 |
| sdk | 54 | 198 | 268 | 1 | 12 | 0 |
| support | 59 | 22 | 246 | 1 | 0 | 1 |
| telephony | 75 | 17 | 223 | 0 | 0 | 0 |
| Tomcat | 336 | 154 | 1284 | 1 | 10 | 1 |
| tool-base | 119 | 69 | 532 | 0 | 0 | 0 |
| Machine learning | Divergent Change | Shotgun Surgery | Parallel Inheritance | |||
| algorithm | Training time (s) | Testing time (s) | Training time (s) | Testing time (s) | Training time (s) | Testing time (s) |
| SMO | 0.08 | 0.05 | 0.04 | 0.02 | 0.48 | 0.14 |
| NaiveBayes | 0.10 | 0.05 | 0.04 | 0.03 | 0.17 | 0.13 |
| J48 | 0.08 | 0.05 | 0.04 | 0.01 | 0.10 | 0.05 |
| JRip | 0.08 | 0.05 | 0.04 | 0.03 | 0.09 | 0.05 |
| RandomForest | 0.07 | 0.05 | 0.04 | 0.02 | 0.10 | 0.05 |
| Logistic Regression | 0.08 | 0.04 | 0.04 | 0.02 | 0.09 | 0.04 |
| Machine learning | Divergent Change | Shotgun Surgery | Parallel Inheritance | |||||||||
| algorithm | Precision | Recall | F-measure | ROC | Precision | Recall | F-measure | ROC | Precision | Recall | F-measure | ROC |
| (%) | (%) | (%) | (%) | (%) | (%) | (%) | (%) | (%) | ||||
| SMO | 72.7 | 72.7 | 72.7 | 0.817 | 100 | 83.3 | 90.9 | 0.917 | 81.8 | 81.8 | 81.8 | 0.887 |
| NaiveBayes | 60.0 | 81.8 | 69.2 | 0.901 | 83.3 | 83.3 | 83.3 | 0.949 | 60.0 | 54.5 | 57.1 | 0.788 |
| J48 | 60.0 | 81.8 | 69.2 | 0.838 | 71.4 | 83.3 | 76.9 | 0.821 | 69.2 | 81.8 | 75.0 | 0.885 |
| JRip | 64.3 | 81.8 | 72.0 | 0.885 | 75.0 | 50.0 | 60.0 | 0.712 | 88.9 | 72.7 | 80.0 | 0.853 |
| RandomForest | 77.8 | 63.6 | 70.0 | 0.903 | 100 | 75.0 | 85.7 | 0.817 | 77.8 | 63.6 | 70.0 | 0.937 |
| Logistic Regression | 81.8 | 81.8 | 81.8 | 0.895 | 100 | 83.3 | 90.9 | 1.000 | 81.8 | 81.8 | 81.8 | 0.935 |
| Machine learning algorithm | Divergent Change | Shotgun Surgery | Parallel Inheritance |
| SMO | 0.0251 | 0.7081 | 0.7359 |
| NaiveBayes | 0.2688 | 0.1895 | 0.0121 |
| J48 | 0.1411 | 0.0209 | 0.2769 |
| JRip | 0.2585 | 0.0054 | 0.6659 |
| RandomForest | 0.7943 | 0.2768 | 0.2802 |
| Machine learning | Divergent Change | Shotgun Surgery | Parallel Inheritance | |||||||||
| algorithm | Precision | Recall | F-measure | ROC | Precision | Recall | F-measure | ROC | Precision | Recall | F-measure | ROC |
| (%) | (%) | (%) | (%) | (%) | (%) | (%) | (%) | (%) | ||||
| SMO-o | 66.7 | 30.8 | 42.1 | 0.621 | 50.0 | 33.3 | 40.0 | 0.635 | 66.7 | 42.9 | 52.2 | 0.679 |
| NaiveBayes-o | 66.7 | 22.2 | 33.3 | 0.595 | 50.0 | 33.3 | 40.0 | 0.750 | 57.1 | 57.1 | 57.1 | 0.776 |
| J48-o | 50.0 | 44.4 | 47.1 | 0.513 | 12.5 | 33.3 | 18.2 | 0.375 | 66.7 | 42.9 | 52.2 | 0.679 |
| JRip-o | 66.7 | 22.2 | 33.3 | 0.596 | 12.5 | 33.3 | 18.2 | 0.375 | 62.5 | 35.7 | 45.5 | 0.643 |
| RandomForest-o | 66.7 | 44.4 | 53.3 | 0.809 | 40.0 | 66.7 | 50.0 | 0.646 | 66.7 | 28.6 | 40.0 | 0.619 |
| Logistic Regression-o | 50.0 | 33.3 | 40.0 | 0.552 | 40.0 | 66.7 | 50.0 | 0.646 | 56.3 | 64.3 | 60.0 | 0.757 |
| Machine learning algorithm | Divergent Change | Shotgun Surgery | Parallel Inheritance |
| SMO-o | 0.0309 | 0.0009 | 0.0059 |
| NaiveBayes-o | 0.0539 | 0.0088 | 0.9619 |
| J48-o | 0.0046 | 0.0002 | 0.0275 |
| JRip-o | 0.0387 | 0.0033 | 0.0085 |
| RandomForest-o | 0.2013 | 0.0098 | 0.0514 |
| Logistic Regression-o | 0.0003 | 0.0021 | 0.0069 |
| Code smell | Precision (%) | Recall (%) | F-measure (%) | ROC area | P-value |
| Divergent Change | 76.9 | 90.9 | 83.3 | 0.903 | 0.6856 |
| Shotgun Surgery | 100 | 83.3 | 90.9 | 1.000 | 1.0000 |
| Parallel Inheritance | 81.8 | 81.8 | 81.8 | 0.943 | 0.9642 |
| Code smell | Precision (%) | Recall (%) | F-measure (%) | ROC area | P-value |
| Divergent Change | 81.8 | 81.8 | 81.8 | 0.881 | 0.8927 |
| Shotgun Surgery | 100 | 83.3 | 90.9 | 1.000 | 1.0000 |
| Parallel Inheritance | 81.8 | 81.8 | 81.8 | 0.937 | 0.9908 |
| Number | Divergent Change | Shotgun Surgery | Parallel Inheritance | |||
| Metric | Coefficients | Metric | Coefficients | Metric | Coefficients | |
| 1 | is interface | 1.4067 | TCC | 46.5202 | BUR | 2.4688 |
| 2 | is root | 1.2052 | is static | 7.6223 | is static | 1.6495 |
| 3 | PNAS | 1.1199 | DOIH | 2.1524 | is leaf class | 1.3356 |
| 4 | is leaf class | 1.0874 | DIT | 1.6331 | is interface | 1.1756 |
| 5 | BUR | 1.0834 | NAM | 1.3989 | MIC | 1.1132 |
| 6 | AMW | 1.0525 | NOC | 1.3080 | NOCON | 1.1101 |
| 7 | DAC | 1.0422 | FDP | 1.1255 | CRIX | 1.0605 |
| 8 | CDC | 1.0333 | AMW | 1.1252 | DAC | 1.0536 |
| 9 | CRIX | 1.0265 | ChC | 1.0140 | NAM | 1.0448 |
| 10 | MIC | 1.0203 | CM | 1.0035 | NOC_Pa | 1.0007 |
| Code smell | Precision (%) | Recall (%) | F-measure (%) | ROC area | P-value |
| Divergent Change | 100 | 50.0 | 66.7 | 0.833 | 0.4549 |
| Shotgun Surgery | 100 | 66.7 | 80.0 | 1.000 | 0.4786 |
| Parallel Inheritance | 60.0 | 30.0 | 40.0 | 0.603 | 0.0037 |
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