@article {MTMT:31336850,
	title = {A public unified bug dataset for java and its assessment regarding metrics and bug prediction},
	journal = {SOFTWARE QUALITY JOURNAL},
	volume = {28},
	year = {2020},
	pages = {1447-1506},
	abstract = {Bug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metric-based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction. We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction. We found that there are statistically significant differences in the values of the original and the newly calculated metrics; furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.},
	keywords = {Bug dataset, Bug prediction, Code metrics, Static code analysis},
	issn = {0963-9314},
	doi = {10.1007/s11219-020-09515-0},
	url = {https://m2.mtmt.hu/api/publication/31336850},
	author = {Ferenc, Rudolf and T{\'o}th, Zolt{\'a}n G{\'a}bor and Lad{\'a}nyi, Gergely and Siket, Istv{\'a}n and Tibor Gyim{\'o}thy}
}
@conference {LHF14,
	title = {The Connection of the Bug Density and Maintainability of Classes},
	booktitle = {8th International Workshop on Software Quality and Maintainability},
	series = {SQM},
	year = {2014},
	note = {Presentation only},
	month = {feb},
	address = {Antwerp, Belgium},
	abstract = {Measuring software product maintainability is a central issue in software engineering which led to a number of different practical quality models. Besides system level assessments it is also desirable that these models provide technical quality information at source code element level (e.g. classes, methods) to aid the improvement of the software. Although many existing models give an ordered list of source code elements that should be improved, it is unclear how these improvements affect other important quality indicators of the system, e.g. bug density. In this paper we empirically investigate the connection of bug numbers in the classes of different open-source systems and the class level maintainability measures of our ColumbusQM probabilistic quality model using open-access bug datasets. We show that classes with lower maintainability score contain significantly more bugs than more maintainable classes. Moreover, in terms of correctness and completeness, ColumbusQM competes with statistical and machine learning prediction models especially trained on the bug data using product metrics as predictors. This is a great achievement in the light of that our model needs no training and its purpose is different (e.g. to estimate testability, or development costs) than those of the bug prediction models. In summary, we show that improving the maintainability of a software according to the suggestions of our quality model will expectedly also reduce its bug density.},
	keywords = {Bug density, Bug prediction, Class level maintainability, ColumbusQM, ISO/IEC 25010, Software maintainability},
	author = {Lad{\'a}nyi, Gergely and P Heged{\H u}s and Rudolf Ferenc and Istv{\'a}n Siket and Tibor Gyim{\'o}thy}
}
