Object oriented based technique for software quality prediction through clustering and chi-square test

In this paper we present an efficient approach for software quality prediction. We accept object oriented modularity as the dataset. The data used for the experimentation have class, object, inheritance and dynamic behavior. After that we categorized our framework for selecting the modularity from six different choices. The six different choices are 1-10, 11-20, 21-30, 31-40, 41-50 and > 50. Procedure for chi-square test is selected by the user. Were the deviations (differences between observed and expected) the result of chance, or were they due to other factors. How much deviation can occur before you, the investigator, must conclude that something other than chance is at work, causing the observed to differ from the expected? The chi-square test is always testing what scientists call the null hypothesis, which states that there is no significant difference between the expected and observed result. Then based on four different object oriented parameters that is class, object, inheritance and dynamic behavior we find chi square probability distribution that is p. Then we process the data that is P value for software quality estimation. For software quality estimation we apply F-measure (FM), Power (PO) and OddRatio (OR).