Refactoring and Detection of Bad Smells of Coding Using Larger Scale and Critical Incident Technique

The presence of code and design smells can have a severe impact on the quality of a program. Consequently, their detection and correction have drawn the attention of both researchers and practitioners who have proposed various approaches to detect code and design smells in programs. However, none of these approaches handle the inherent uncertainty of the Detection process. First, we present a system-matic process to convert existing state-of-the-art detection rules into a probabilistic model. We illustrate this process by generating a model to detect occurrences of the Blob anti-pattern. Second, we present results of the validation of the model. Testing is more than just debugging. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Testing can be used as a generic metric as well. Correctness testing and reliability testing are two major areas of testing. Software testing is a trade-off between budget, time and quality. Code smells are a metaphor to describe patterns that are generally associated with bad design and bad programming practices. Originally, code smells are used to nd the places in software that could benet from refactoring. . Refactoring is a technique to make a computer program more readable and maintainable. A bad smell is an indication of some setback in the code, which requires refactoring to deal with. Many tools are available for detection and removal of these code smells. These tools vary greatly in detection methodologies and acquire different competencies. In this paper, how the quality of code can be automatically assessed by checking for the presence of code smells is and how this approach can contribute to automatic code inspection is investigated