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Examining the Bug Prediction Capabilities of Primitive Obsession Metrics

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12955))

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Abstract

Bug prediction is an approach that helps make bug detection more automated during software development. Based on a bug dataset a prediction model is built to locate future bugs. Bug datasets contain information about previous defects in the code, process metrics, or source code metrics, etc. As code smells can indicate potential flaws in the source code, they can be used for bug prediction as well.

In our previous work, we introduced several source code metrics to detect and describe the occurrence of Primitive Obsession in Java. This paper is a further study on three of the Primitive Obsession metrics. We integrated them into an existing, source code metrics-based bug dataset, and studied the effectiveness of the prediction built upon it. We performed a 10 fold cross-validation on the whole dataset and a cross-project validation as well. We compared the new models with the results of the original dataset. While the cross-validation showed no significant change, in the case of the cross-project validation, we have found that the amount of improvement exceeded the amount of deterioration by 5%. Furthermore, the variance added to the dataset was confirmed by correlation and PCA calculations.

This work was partially supported by grant 2018-1.2.1-NKP-2018-00004 “Security Enhancing Technologies for the IoT” funded by the Hungarian National Research, Development and Innovation Office. It was also supported by the European Union, co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002). The research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program (MILAB).

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Notes

  1. 1.

    https://github.com/stevenalowe/kata-2-tinytypes.

  2. 2.

    The precision can be calculated as the number of true positive elements divided by the sum of the true and false positive elements. It describes what portion of the identifications was actually correct.

  3. 3.

    It describes what portion of the actual relevant elements was identified correctly.

  4. 4.

    F-measure is the harmonic mean of precision and recall.

  5. 5.

    The ROC curve displays the performance of a classification model at all classification threshold. The Area under the ROC curve (AUC) means the area under this curve. It is an aggregated measure of the performance across every classification thresholds.

  6. 6.

    Weka calculates the Weighted Avg. F-Measure with the following formula for an n and y class: \(Weighted\ Avg.\ F-measure= \frac{F-measure(n) \cdot NumOfInstances(x) + F-measure(y) \cdot NumOfInstances(y)}{NumOfInstances(n) + NumOfInstances(y)}\), where NumOfInstances(n), NumOfInstances(y) correspond to the number of instances in the given class.

  7. 7.

    Correlation shows the degree to which a pair of variables are linearly related. We used Pearson correlation for our experiment.

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Authors and Affiliations

  1. University of Szeged, Dugonics square 13, Szeged, 6720, Hungary

    Edit Pengő

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  1. Edit Pengő
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Correspondence to Edit Pengő .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Pengő, E. (2021). Examining the Bug Prediction Capabilities of Primitive Obsession Metrics. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_14

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