Detecting sudden variations in web apps code smells’ density: A longitudinal study

• Main Author: Rio, A.
• Publication Date: 2021
• Other Authors: Abreu, F. B. e.
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10071/24447
• topic: PHP; Code smells; Web apps; Sudden variations; Anomaly detection; Outliers

Code smells are considered potentially harmful to software maintenance. Their introduction is dependent on the production of new code or the addition of smelly code produced by another team. Code smells survive until being refactored or the code where they stand is removed. Under normal conditions, we expect code smells density to be relatively stable throughout time. Anomalous (sudden) increases in this density are expected to hurt maintenance costs and the other way round. In the case of sudden increases, especially in pre-release tests in an automation server pipeline, detecting those outlier situations can trigger refactoring actions before releasing the new version. This paper presents a longitudinal study on the sudden variations in the introduction and removal of 18 server code smells on 8 PHP web apps, across several years. The study regards web applications but can be generalized to other domains, using other CS and tools. We propose a standardized detection criterion for this kind of code smell anomalies. Besides providing a retrospective view of the code smell evolution phenomenon, our detection approach, which is particularly amenable to graphical monitoring, can make software project managers aware of the need for enforcing refactoring actions.