Automatic Multiprogramming Bad Smell Detection with Refactoring

A code smell detection and refactor is one of the very hot concepts in these days. A Lot of researcher worked on it to create an automatic bad smell detection and refactoring system. Main purpose behind the development of these type of systems is to create automatic for enhance the development quality of software systems. In the previous research the smell detection system perform detection on specific areas or specific language. Due to this companies needs to use more than one detector for software testing for large projects. The system is combination of various modules which can be developed in various languages. Our proposed method which is helpful their users to test their code and detect bad smell on more than one language. It acts as a bridge with some optimization techniques which provide highly accurate working for smell detection along with refactoring. Proposed approach uses optimization along with fact and rule programming to detect and refactor the bad smell from input programs. Various bad smells like long methods, dead code, lazy class, long class, etc. are used to check the quality of the code. The proposed approach is also working for Java, c++ and c#. net codes for the test all these bad smell and refactor c++ and Java code. The performance of the proposed approach is also better than other existing algorithms in terms of accuracy for detection and refactoring of bad smells. Some other challenges that the proposed approach faced to find the smells in the code also affect the performance. One of the main challenges is the way of writing code is different for everyone. So it's difficult to detect and refactor the thing on smell detection tool. Proposed approach used fact and rule processing for detection and eliminates unwanted entries with the help of the optimization process. The performance in terms of accuracy and FAR, FRR are stable and better for all the test cases in the comparison of existing methods and proposed approach. (C) 2017 The Authors. Published by IASE. This is an open access article under the CC BY-NC-ND license.