Automatic detection of architectural bad smells through semantic representation of code-Reference-Cited by-同舟云学术

Automatic detection of architectural bad smells through semantic representation of code

Published:2019 Issue: Volume: Page:
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Container-title:Proceedings of the 13th European Conference on Software Architecture - ECSA '19 - volume 2
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Author:

Pigazzini Ilaria¹

Affiliation:

1. University of Milano - Bicocca, Milano, Italy

Publisher

ACM Press

Reference19 articles.

1. Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. Code2Vec: Learning Distributed Representations of Code. Proc. ACM Program. Lang. (2019).

2. Alvine Boaye Belle, Ghizlane El Boussaidi, and Sègla Kpodjedo. 2016. Combining Lexical and Structural Information to Reconstruct Software Layers. Inf. Softw. Technol. 74, C (June 2016), 1--16.

3. Anna Corazza, Sergio Martino, Valerio Maggio, and Giuseppe Scanniello. 2016. Weighing Lexical Information for Software Clustering in the Context of Architecture Recovery. Empirical Softw. Engg. 21, 1 (Feb. 2016), 72--103.

4. Hugo Sica de Andrade, Eduardo Almeida, and Ivica Crnkovic. 2014. Architectural Bad Smells in Software Product Lines: An Exploratory Study. In Proc. of the WICSA 2014 Companion Volume. ACM.

5. Lakshitha de Silva and Dharini Balasubramaniam. 2012. Controlling software architecture erosion: A survey. Journal of Systems and Software 85, 1 (2012).

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