Code smells and quality attributes dataset

1 Code smell datasetIn order to create a high quality code smell datasets, we merged five different datasets. These datasets are among the largest and most accurate in our paper “Predicting Code Quality Attributes Based on Code Smells ”. Various software projects were analyzed automatically and manually to collect these labels. Table 1 shows the dataset details.
Table 1. Merged datasets and their characteristics.
Dataset
Samples
Projects
Code smells
Palomba (2018) [1]
40888
395 versions of 30 open-source projects
Large class, complex class, class data should be private, inappropriate intimacy, lazy class, middle man, refused equest, spaghetti code, speculative generality, comments, long method, long parameter list, feature envy, message chains
Madeyski [2]
3291
523 open-source and industrial projects
Blob, data class
Khomh [3]
_
54 versions of 4 open-source projects
Anti-singleton, swiss army knife
Pecorelli [4]
341
9 open-source projects
Blob
Palomba (2017) [5]
_
6 open-source projects
Dispersed coupling, shotgun surgery

Code smell datasets have been prepared at two levels: class and method. The class level is 15 different smells as labels and 81 software metrics as features. As well, there are five smells and 31 metrics on the method level. This dataset contains samples of Java classes and methods. A sample can be identified by its longname, which contains the project-name, package-name, JavaFile-name, class-name, and method-name. The quantity of each smell ranges from 40 to 11000. The total number of samples is 37517, while the number of non-smells is nearly 3 million. As a result, our dataset is the largest in the study. You can see the details in Table 2.
Table 2. The number of smells and non-smells at class and method levels
Level
Metrics
Smell
Samples
Total
Class
81
Complex class
1265
23438
Class data should be private
1839
Inappropriate intimacy
780
Large class
990
Lazy class
774
Middle man
193
Refused bequest
1985
Spaghetti code
3203
Speculative generality
2723
Blob
988
Data class
938
Anti-singleton
2993
Swiss army knife
4601
Dispersed coupling
41
Shotgun surgery
125
Non-smell
40506 [3] +
8334 [5] +
296854 [1]+
43862 [2] +
55214 [4]
444770
Method
31
Comments
107
14079
Feature envy
525
Long method
11366
Long parameter list
1983
Message chains
98
Non-smell
2469176
2469176

2 Quality datasetThis dataset contains over 1000 Java project instances where for each instance the relative frequency of 20 code smells has been extracted along with the value of eight software quality attributes. The code quality dataset contains 20 smells as features and 8 quality attributes as labels: Coverageability, extendability, effectiveness, flexibility, functionality, reusability, testability, and understandability. The samples are Java projects identified by their name and version. Features are the ratio of smelly and non-smelly classes or methods in a software project. The quality attributes are a normalized score calculated by QMOOD metrics [6] and models extracted by [7], [8]. 1014 samples of small and large open-source and industrial projects are included in this dataset.
The data samples are used to train machine learning models predicting software quality attributes based on code smells.
References[1] F. Palomba, G. Bavota, M. Di Penta, F. Fasano, R. Oliveto, and A. De Lucia, “A large-scale empirical study on the lifecycle of code smell co-occurrences,” Inf Softw Technol, vol. 99, pp. 1–10, Jul. 2018, doi: 10.1016/J.INFSOF.2018.02.004.
[2] L. Madeyski and T. Lewowski, “MLCQ: Industry-Relevant Code Smell Data Set,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Apr. 2020, pp. 342–347. doi: 10.1145/3383219.3383264.
[3] F. Khomh, M. Di Penta, Y. G. Guéhéneuc, and G. Antoniol, “An exploratory study of the impact of antipatterns on class change- and fault-proneness,” Empir Softw Eng, vol. 17, no. 3, pp. 243–275, Jun. 2012, doi: 10.1007/s10664-011-9171-y.
[4] F. Pecorelli, F. Palomba, F. Khomh, and A. De Lucia, “Developer-Driven Code Smell Prioritization,” Proceedings - 2020 IEEE/ACM 17th International Conference on Mining Software Repositories, MSR 2020, pp. 220–231, 2020, doi: 10.1145/3379597.3387457.
[5] F. Palomba, M. Zanoni, F. A. Fontana, A. De Lucia, and R. Oliveto, “Smells like teen spirit: Improving bug prediction performance using the intensity of code smells,” in Proceedings - 2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016, Institute of Electrical and Electronics Engineers Inc., Jan. 2017, pp. 244–255. doi: 10.1109/ICSME.2016.27.
[6] J. Bansiya and C. G. Davis, “A hierarchical model for object-oriented design quality assessment,” IEEE Transactions on Software Engineering, vol. 28, no. 1, pp. 4–17, Jan. 2002, doi: 10.1109/32.979986.
[7] M. Zakeri-Nasrabadi and S. Parsa, “Learning to predict test effectiveness,” International Journal of Intelligent Systems, 2021, doi: 10.1002/INT.22722.
[8] M. Zakeri-Nasrabadi and S. Parsa, “Testability Prediction Dataset,” Mar. 2021, doi: 10.5281/ZENODO.4650228.