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

Name: Using artificial neural networks to provide guidance in extending PL/SQL programs
Author: Ersoy, E., Sözer, Hasan

İsim	Using artificial neural networks to provide guidance in extending PL/SQL programs
Yazar	Ersoy, E., Sözer, Hasan
Basım Tarihi:	2022-12
Basım Yeri	- Springer
Konu	Architecture erosion, Artificial neural networks, Industrial case study, Software architecture, Software maintenance
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0963-9314
Kayıt Numarası	28ead14f-f943-4600-9f6d-1391696285eb
Lokasyon	Computer Science
Tarih	2022-12
Örnek Metin	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 Misplacement of these objects gradually hampers the modular structure, of which documentation is usually missing or outdated.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.
DOI	10.1007/s11219-022-09586-1
Cilt	30

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

Yazar Ersoy, E., Sözer, Hasan

Basım Tarihi 2022-12

Basım Yeri - Springer

Konu Architecture erosion, Artificial neural networks, Industrial case study, Software architecture, Software maintenance

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0963-9314

Kayıt Numarası 28ead14f-f943-4600-9f6d-1391696285eb

Lokasyon Computer Science

Tarih 2022-12

Örnek Metin 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.

DOI 10.1007/s11219-022-09586-1

Cilt 30