Code Smells Detection Using Artificial Intelligence Techniques: A Business-Driven Systematic Review

Abstract

Context Code smells in the software systems are indications that usually correspond to deeper problems that can negatively influence software quality characteristics. This review is a part of a R&D project aiming to improve the existing codebeat platform that help developers to avoid code smells and deliver quality code. Objective This study aims to identify and investigate the current state of the art with respect to: (1) predictors used in prediction models to detect code smells, (2) machine learning/artificial intelligence (ML/AI) methods used in prediction models to detect code smells, (3) code smells analyzed in scientific literature. Our secondary objectives were to identify (4) data sets and projects used in research papers to predict code smells, (5) performance measures used to assess prediction models and (6) improvement ideas with regard to code smell detection using ML/AI. Method We conducted a systematic review using a database search in Scopus and evaluated it using the quasi-gold standard procedure to identify relevant studies. In the data sheet used to obtain data from publications we factor research questions into finer-grained ones, which are then answered on a per-publication basis. Those are then merged over a set of publications using an automated script to obtain answers to the posed research questions. Results We have identified 45 primary studies relevant to the primary objectives of this research. The results show the prediction capability of the ML/AI techniques for predicting code smells. Conclusion Only a few smells—Blob, Feature Envy, Long Method and Data Class—have received the vast majority of interest in research community. The usage of deep learning techniques is increasing. Most researchers still use source code metrics as predictors. Precision, recall and F-measure are the go-to performance metrics. There seems to be a need for modern reference data/projects sets that reflect modern constructs of programming languages. We identified various promising paths of research that have the potential to advance the state of the art in the area of code smells prediction.