Machine/deep learning-based code smell detection aims to develop a classification model based on code smell features to predict the presence of code smell in new code instances. To ensure accurate detection, it is crucial to eliminate irrelevant or redundant features that may negatively impact performance. Previous studies have produced inconsistent findings about the impact of feature selection techniques for code smell detection, possibly because they examined only a limited number of different techniques. To address this gap, our study aims to provide a comprehensive analysis of feature selection techniques in code smell detection. We investigate 34 feature selection techniques with 7 classification models to build the code smell detection models on 6 code smell datasets. To assess these effects, we use 3 evaluation metrics, i.e., Precision, Recall, and F-measure, and compare the performance differences using the Scott-Knott effect size difference test and the McNemar's test. The results show that (1) Not all feature selection techniques significantly improve detection performance. The techniques with better performance are chi-square, probabilistic significance, information gain, and symmetrical uncertainty. (2) In general, probabilistic significance should be used as the “generic” feature selection technique because detection models using probabilistic significance can identify more of the same smelly instances compared to models using other methods. (3) The high-frequency features selected by the four highest-performing techniques, which are important for identifying the corresponding code smells, are different for each dataset.

Code smell detection can automatically identify code smells in software source code to help developers to improve code maintainability, readability, and overall code quality. Currently, a wide variety of code smell detection techniques/tools are proposed for practical use. However, it is unclear what practitioners expect for code smell detection tools and whether the existing research meets their needs. To fill the gap, we conduct an empirical study. We first interview 10 software development professionals and subsequently survey 310 software practitioners about their practices and expectations of code smell detection tools. In addition, we conduct an extensive literature review of code smell detection papers published in major publications from 2014 to 2024, and compare current research findings with practitioners' expectations. From this comparison, we highlight the direction in which researchers need to work to develop code smell detection techniques that are important to practitioners.

UltraFlow - also known as Optical Flow Switching (OFS) has been recently presented as an effective technology for large Internet data transfer. In this paper, we propose a novel scheduling mechanism for the Stanford UltraFlow access network, a novel optical access network architecture that offers dual-mode service to the end-users: legacy IP and OFS. The proposed scheme is a hybrid mechanism that combines the batch scheduling technique with existing methods so as to increase the average Flow throughput, while maintaining relatively low traffic latency. Extensive simulations highlight the advantages of the proposed solution and demonstrate its merits.