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Using artificial neural networks to provide guidance in extending PL/SQL programs

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Abstract

Extending legacy systems with new objects for contemporary functionality or technology can lead to architecture erosion. Misplacement of these objects gradually hampers the modular structure, of which documentation is usually missing or outdated. In this work, we aim at addressing this problem for PL/SQL programs, which are highly coupled with databases. We propose a novel approach that employs artificial neural networks to automatically predict the correct placement of a new object among architectural modules. We train a network based on features extracted from the initial version of the source code that is assumed to represent the intended architecture. We use dependencies among the software and database objects as features for this training. Then, given a new object and the list of other objects it uses, the network can predict the architectural module, where the object should be included. We performed two industrial case studies with applications from the telecommunications domain, each of which involves thousands of procedures and database tables. We showed that the accuracy of our approach is 86.7% and 89% for these two applications. The baseline approach that uses coupling and cohesion metrics reaches 55.5% and 57.4% accuracy for the same applications, respectively.

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Change history

  • 16 April 2022

    The original version of this article was updated to correct the article title.

Notes

  1. http://www.oracle.com

  2. http://www.turkcell.com.tr

  3. https://scikit-learn.org/stable/modules/model_evaluation.html#accuracy-score

  4. https://scikit-learn.org/stable/modules/model_evaluation.html#multiclass-and-multilabel-classification

  5. https://github.com/ersinersoy/annplsqlclassification

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Acknowledgements

We would like to thank software developers at Turkcell for sharing their code base with us and supporting our case studies. We would also like to thank the anonymous reviewers, who helped us to improve the quality of this paper significantly.

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Correspondence to Ersin Ersoy.

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Appendix

Appendix

Top ten accuracy values achieved with the datasets for CRM application CRM-T, CRM-TV, CRM-TVPF and CRM-ALL are depicted in Table 18. There are two parts in each cell of the Table 18 for each dataset. The first part shows the best hyperparameters for the top 10 results. The second part shows the results for each of these settings.

Table 18 Top 10 accuracy values obtained with CRM case study datasets, and the corresponding ANN hyperparameters
Table 19 Top 10 accuracy values obtained with CMS case study datasets, and the corresponding ANN hyperparameters

Hereby, the first row of the second part lists the accuracy scores. The second row of the second part list macro scores of precision, recall and F1. Finally, third row of the second part list weighted scores of precision, recall and F1. Also, top ten accuracy values achieved with the datasets for CMS application CMS-T, CMS-TVPF and CMS-ALL are depicted in Table 19.

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Ersoy, E., Sözer, H. Using artificial neural networks to provide guidance in extending PL/SQL programs. Software Qual J (2022). https://doi.org/10.1007/s11219-022-09586-1

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  • DOI: https://doi.org/10.1007/s11219-022-09586-1

Keywords

  • Software architecture
  • Software maintenance
  • Architecture erosion
  • Artificial neural networks
  • Industrial case study