Classification of Software Bugs using Support Vector Machine

Support Vector Machine (SVM) is a most widely used classification technique in machine learning as it gives a better accuracy than most of the existing techniques. These days, users frequently run across glitches and malfunctions when using software or applications. A lot more understanding is required by the developer to identify the type of bug, which takes time. In this system, to find the type of a bug a machine learning classification technique SVM. Here, bug report is fed as an input to a natural language processing algorithm. In this method, a dataset containing various types of bug reports is used to analyse the description in the given bug report using text processing and keyword extraction in natural language processing. This model can identify three types of bugs: memory bugs, concurrency bugs and semantic bugs using SVM. This system proposes a SVM model that gives better accuracy than the existing models which use several other techniques like decision tree, random forest and naive bayes.