  Background: Defect prediction in software can be highly beneficial for
development projects, when prediction is highly effective and defect-prone
areas are predicted correctly. One of the key elements to gain effective
software defect prediction is proper selection of metrics used for dataset
preparation. Objective: The purpose of this research is to verify, whether code
smells metrics, collected using Microsoft CodeAnalysis tool, added to basic
metric set, can improve defect prediction in industrial software development
project. Results: We verified, if dataset extension by the code smells sourced
metrics, change the effectiveness of the defect prediction by comparing
prediction results for datasets with and without code smells-oriented metrics.
In a result, we observed only small improvement of effectiveness of defect
prediction when dataset extended with bad smells metrics was used: average
accuracy value increased by 0.0091 and stayed within the margin of error.
However, when only use of code smells based metrics were used for prediction
(without basic set of metrics), such process resulted with surprisingly high
accuracy (0.8249) and F-measure (0.8286) results. We also elaborated data
anomalies and problems we observed when two different metric sources were used
to prepare one, consistent set of data. Conclusion: Extending the dataset by
the code smells sourced metric does not significantly improve the prediction
effectiveness. Achieved result did not compensate effort needed to collect
additional metrics. However, we observed that defect prediction based on the
code smells only is still highly effective and can be used especially where
other metrics hardly be used.
