Rajwant Singh Rao

• Doctor of Philosophy
• Professor (Assistant) at Guru Ghasidas Vishwavidyalaya

Code smells are software flaws that make it challenging to comprehend, develop, and maintain the software. Identifying and removing code smells is crucial for software quality. This study examines the effectiveness of several machine-learning models before and after applying feature selection and data balancing on code smell datasets. Extreme Gradi...

Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using...

(1) Background: Code smell is the most popular and reliable method for detecting potential errors in code. In real-world circumstances, a single source code may have multiple code smells. Multi-label code smell detection is a popular research study. However, limited studies are available on it, and there is a need for a standardized classifier for...

Detecting code smells may be highly helpful for reducing maintenance costs and raising source code quality. Code smells facilitate developers or researchers to understand several types of design flaws. Code smells with high severity can cause significant problems for the software and may cause challenges for the system’s maintainability. It is quit...

Code smell detection is critical for calculating system quality and identifying issues that require more work and development. The technique of finding wrongly developed code components and implementing them is known as code smell detection. In this study, we used two method-level code smell datasets: the long parameter list and the switch statemen...

Code smell detection can be very useful for minimizing maintenance costs and improving software quality. Code smells help developers/programmers, researchers to subjectively interpret design defects in different ways. Code smells instances can have varied size, intensity or severity which needs to be focused upon as they affect the software quality...

Code smells are the result of not following software engineering principles during software development, especially in the design and coding phase. It leads to low maintainability. To evaluate the quality of software and its maintainability, code smell detection can be helpful. Many machine learning algorithms are being used to detect code smells....

Wireless technology, especially Vehicular Ad-hoc Network (VANET), is developing rapidly. VANET is an emergingtechnology that assists intelligent transportation systems by improving traffic services and helping in minimizing roadaccidents. Data sharing in VANETs is time-critical, necessitating the formation of fast and robust network connections.Due...

Code smells combine software features that indicate a code or design-related problem that can increase the difficulty of the software and make it complicated to recognize, develop, and maintain the software. Code smell detection helps us to make code accurate and effective. In this study, five classification models are applied. To complete this stu...

Vehicular Ad-hoc Network (VANET) is an essential part of futuristic Intelligent Transportation Systems. VANET can improve the overall traffic control system and reduce road accident deaths by providing remote health monitoring in hazardous conditions to outdoor patients. Nowadays, vehicles have become so intelligent that they can sense patient heal...

Code Smell refers to the telltale signs of poor code design that leads to software quality issues. Developers require specific methods to measure the complexity of Code Smells in order to resolve the problem quickly. Recent research has examined the problem of predicting Code Smell using various detection methods. However, the accuracy of machine l...

Code smells detection helps in improving understandability and maintainability of software while reducing the chances of system failure. In this study, six machine learning algorithms have been applied to predict code smells. For this purpose, four code smell datasets (God-class, Data-class, Feature-envy, and Long-method) are considered which are g...

Design Pattern Detection is a part of many solutions to Software Engineering problems. It is a part of reengineering process and thus gives significant information to the designer. Design Pattern improves the program understanding and software maintenance. Therefore, a reliable design pattern discovery is required. Graph theoretic approaches have b...

Design Pattern Detection has been documented so far in the literature. The knowledge of design Pattern existence in the program improves the program understanding and software maintenance. Design pattern is a technology for design reuse. Experts store their experiences in the form of design patterns. Reengineering done by novice users will be succe...

Design Patterns are proven solution to common recurring design problems. Design Pattern Detection is most important activity that may support a lot to re-engineering process and thus gives significant information to the designer. Knowledge of design pattern exists in the system design improves the program understanding and software maintenance. The...