In the domain of the software fault prediction numerous methods have been introduced and implemented using data mining techniques and machine learning models. Nevertheless, initial and early fault prediction is big challenging to be overcome and improvised with higher classification rate of fault prediction. In order to fix this issue several software solutions and approaches are suggested by many researchers. In this paper a comparative study is done amongst all these approaches. Some software solutions for defect prediction are based on the software metrics evaluation but they are time consuming. To resolve this considerable time complexity many researchers suggested the software defect prediction model incorporating the machine learning model and got the better result.