Nowadays, software developers are facing challenges in minimizing the number of defects during the software development. Using defect density parameter, developers can identify the possibilities of improvements in the product. Since the total number of defects depends on module size, so there is need to calculate the optimal size of the module to minimize the defect density. In this paper, an improved model has been formulated that indicates the relationship between defect density and variable size of modules. This relationship could be used for optimization of overall defect density using an effective distribution of modules sizes. Three available data sets related to concern aspect have been examined with the proposed model by taking the distinct values of variables and parameter by putting some constraint on parameters. Curve fitting method has been used to obtain the size of module with minimum defect density. Goodness of fit measures has been performed to validate the proposed model for data sets. The defect density can be optimized by effective distribution of size of modules. The larger modules can be broken into smaller modules and smaller modules can be merged to minimize the overall defect density. 1. Introduction The reliability of a software product is an important factor, being considered by the developer before the formal release of a product. Defect density (DD) is an important attribute that affects software reliability. Defect prediction is very important for estimation of defect density. There are many methods available that can be used to predict the number of defects in software during testing phases [1]. Estimation of this attribute is one of the approaches used to establish readiness for release. There are several methods that can be used to predict or estimate the value of this parameter. Six sigma methods are used by Fehlmann [2] for advanced prediction of defect density for software that has been developed and its development process is moving further for production. Mark [3] has presented a DevCOP method that has been used for estimation of defect density using verification and validation certificate technique. There are a few crucial factors that have an effect on the initial defect density. Analysis of these factors is important because it provides a quantitative method of identifying possible techniques for reducing the insertion rate of defects. Further, this analysis can be used to estimate initial defect density that may be used later while planning the required effort for testing. In present scenario many models are
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