Code smell detection using multi-label classification approach

Exploiting Associations between Class Labels in Multi-label Classification

Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...

Multi-step Automated Refactoring for Code Smell

In computer programming, code smell may origin of latent problems in source code. Detecting and resolving bad smells remain time intense for software engineers despite proposals on bad smell detecting and refactoring tools. Numerous code smells have been recognized yet the sequence in which the detection and resolution of different kinds of code smells are performed because software engineers d...

Correctness of Semantic Code Smell Detection Tools

Refactoring is a set of techniques used to enhance the quality of code by restructuring existing code/design without changing its behavior. Refactoring tools can be used to detect specific code smells, propose relevant refactorings, and in some cases automate the refactoring process. However, usage of refactoring tools in industry is still relatively low. One of the major reasons being the vera...

Code sniffer: a risk based smell detection framework to enhance code quality using static code analysis

To maintain software and enhance its code quality, code smell, i.e., undesired design flaws need to be detected. However, as the system size increases, manual smell detection becomes difficult. In this paper, a static code analysis framework, named code sniffer, is proposed to detect code smells with predicting their risk severity. This has been calculated using code metrics, and defined as low...

A multi-label classification based approach for sentiment classification