Number Of Code Smells Research Articles

Empirical evaluation of code smells in open-source software (OSS) using Best Worst Method (BWM) and TOPSIS approach

PurposeCode smells indicate deep software issues. They have been studied by researchers with different perspectives. The need to study code smells was felt from the perspective of software industry. The authors aim to evaluate the code smells on the basis of their scope of impact on widely used open-source software (OSS) projects.Design/methodology/approachThe authors have proposed a methodology to identify and rank the smells in the source code of 16 versions of Apache Tomcat Software. Further, the authors have analyzed the categorized smells by calculating the weight of the smells using constant weights as well as Best Worst Method (BWM). Consequently, the authors have used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine the rank of versions using constant weights as well as BWM.FindingsVersion 1 of Apache Tomcat has least smell, and version 8 is reported to contain the maximum code smells. Notable differences in both the cases during the trend analysis are reported by the study. The findings also show that increase is observed in the number of code smells with the release of newer versions. This increment is observed till version 8, followed by a subtle marked depreciation in the number of code smells in further releases.Originality/valueThe focus is to analyze smells and rank several versions of Apache Tomcat, one of the most widely used software for code smell study. This study will act as a significant one for the researchers as it prioritizes the versions and will help in narrowing down the options of the software used to study code smell.

Sequential Ordering of Code Smells and Usage of Heuristic Algorithm

The process of removing the bad smell results in introduction of new smells due to dependency between the codes in the program. This process increases human effort and time. Automated tools are used for detecting the bad smells in the program. This problem is called as ripple effect and we aim in reducing and removing this effect in the program. We apply refactoring process for reducing the amount of bad smells in the code. Since there exist more number of code smells in the program, we generate a sequence in which the refactoring has to be applied by which the evolution of new bad smells is enormously decreased. The refactoring methods that have to be applied to the source code are also ordered using a heuristic algorithm.