Detection Strategies of Bad Smells in Highly Configurable Software

Software maintenance is a tough work and when code become very large its hard to track the changes and bad code makes it harder. One way to track the quality of software is to track for bad smells in the software. They can help track the code which can cause problems in near future. The objective is to build a bot that crawls through our code daily and gives a status of smells in the code. To achieve this, developed a set of instruction and strategies and implemented them in python to parse code of java and track the software with time. All statistics are shown with the python library matplotlib and the bot can be automated to crawl in Linux, Mac, and Windows. Found that, the bot can detect smells which are not detectable by the developer. The life of software can be increased by a refracting the code time to time with the help of smells detected by the bot. Only java is supported till now but this paper increased the support to other languages also.