Predicting Code Smells and Analysis of Predictions: Using Machine Learning Techniques and Software Metrics

Code smell detection is essential to improve software quality, enhancing software maintainability, and decrease the risk of faults and failures in the software system. In this paper, we proposed a code smell prediction approach based on machine learning techniques and software metrics. The local interpretable model-agnostic explanations (LIME) algorithm was further used to explain the machine learning model's predictions and interpretability. The datasets obtained from Fontana et al. were reformed and used to build binary-label and multi-label datasets. The results of 10-fold cross-validation show that the performance of tree-based algorithms (mainly Random Forest) is higher compared with kernel-based and network-based algorithms. The genetic algorithm based feature selection methods enhance the accuracy of these machine learning algorithms by selecting the most relevant features in each dataset. Moreover, the parameter optimization techniques based on the grid search algorithm significantly enhance the accuracy of all these algorithms. Finally, machine learning techniques have high potential in predicting the code smells, which contribute to detect these smells and enhance the software's quality.

检测JavaScript类的内聚耦合Code Smell

Code Smell是软件程序中存在不良设计和不良实现的征兆.正确地检测和识别Code Smell可以指导软件重构,提高软件的可用性和可靠性.通过Code Smell的度量指标,可以量化软件的设计问题.JavaScript已成为最常用的编程语言之一,类是JavaScript的设计模式,优秀类的设计体现为高内聚和低耦合.现有关于JavaScript内聚耦合的Code Smell研究均在微观的层面,即函数和语句上进行.它们可以提供程序实现的重构建议,但无法分析内聚耦合相关的软件系统设计问题.针对FE、DC和Blob这3种类的内聚耦合Code Smell,提出一种JavaScript类的内聚耦合Code Smell检测方法JS4C.该方法基于静态分析,同时适用于客户端和服务端程序.它通过遍历软件系统中所有的类,利用源程序的文本相似度特征和结构特征,识别Code Smell并检测其强度.在结构特征检测中,JS4C使用了经扩展的对象类型推断及非严格的耦合分散度度量法NSCDISP,有效地降低了解释型语言的静态分析过程中,类型信息缺失对检测产生的影响.实验通过对6个开源项目的分析表明,JS4C对内聚耦合设计问题有良好的检测效果.

Code Smell Detection Using Whale Optimization Algorithm

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the software industry.They are characteristics of software source code that indicate a deeper problem in design.These smells appear not only in the design but also in software implementation.Code smells introduce bugs,affect software maintainability,and lead to higher maintenance costs.Uncovering code smells can be formulated as an optimization problem of finding the best detection rules.Although researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be improved.Previous research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their types.Our research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection rules.Applying this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass variance.The proposed framework adopts if-then detection rules during the software development life cycle.Those rules identify the types for both medium and large projects.Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes.The proposed detection framework has an average of 94.24%precision and 93.4%recall.These accurate values are better than other search-based algorithms of the same field.The proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and cost.Additionally,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

Code Smell Detection Based on Multi-dimensional Software Data and Complex Networks

Code smell is the product of improper design and operation,which may be introduced in many situations.It will cause serious problems for further software development and maintenance.Currently,most code smell detection methods detect through a single type of software data.There are restrictions on detecting code smells with complex definitions and characteristics.In this paper,an approach of applying multi-dimensional software data is proposed.A complex network was built through structural data and historical version data,and code smell instances were determined by searching the network.We designed two smells detection strategies were designed and evaluated them in four open source projects.The results demonstrate that the proposed method has 23%and 15%higher F-measures on Shotgun Surgery and Parallel Inheritance Hierarchy than the existing mainstream detection ways.The code smell detection based on multi-dimensional software data and complex network is effective,and this method of processing multidimensional software data is also applicable for data-driven software research.