Reuse refers to a common principle of using existing resources repeatedly, that is pervasively applicable everywhere. In software engineering reuse refers to the development of software systems using already available artifacts or assets partially or completely, with or without modifications. Software reuse not only promises significant improvements in productivity and quality but also provides for the development of more reliable, cost effective, dependable and less buggy (considering that prior use and testing have removed errors) software with reduced time and effort. In this paper we present an efficient and reliable automation model for reusability evaluation of procedure based object oriented software for predicting the reusability levels of the components as low, medium or high. The presented model follows a reusability metric framework that targets the requisite reusability attributes including maintainability (using the Maintainability Index) for functional analysis of the components. Further Multilayer perceptron (using back propagation) based neural network is applied for the establishment of significant relationships among these attributes for reusability prediction. The proposed approach provides support for reusability evaluation at functional level rather than at structural level. The automation support for this approach is provided in the form of a tool named JRA2M2 (Java based Reusability Assessment Automation Model using Multilayer Perceptron (MLP)), implemented in Java. The performance of JRA2M2 is recorded using parameters like accuracy, classification error, precision and recall. The results generated using JRA2M2 indicate that the proposed automation tool can be effectively used as a reliable and efficient solution for automated evaluation of reusability.

Defects are most detrimental entities which deter the smooth operation and deployment of the software system and can arise in any part of the life cycle, they are most feared, but still Defect Prevention is mostly discounted field of software quality. Unattended defects cause a lot of rework and waste of effort. Hence only finding the defects is not important, finding the root cause of the defect is also important which is quite difficult due to levels of abstraction in terms of people, process, complexity, environment and other factors. Through this study various techniques of Defect classification, prevention and root cause analysis are analysed. The intent of this paper is to demonstrate the structured process showing defect prevention flow and inferring three T's (Tracking, Technique and Training) after analysis.

Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has eliminated bugs) and accelerated (reduced time to market) development of the software products. In this paper we present an efficient automation model for the identification and evaluation of reusable software components to measure the reusability levels (high, medium or low) of procedure oriented Java based (object oriented) software systems. The presented model uses a metric framework for the functional analysis of the Object oriented software components that target essential attributes of reusability analysis also taking into consideration Maintainability Index to account for partial reuse. Further machine learning algorithm LMNN is explored to establish relationships between the functional attributes. The model works at functional level rather than at structural level. The system is implemented as a tool in Java and the performance of the automation tool developed is recorded using criteria like precision, recall, accuracy and error rate. The results gathered indicate that the model can be effectively used as an efficient, accurate, fast and economic model for the identification of procedure based reusable components from the existing inventory of software resources.

Software repositories contain wealth of information about software code, designs, execution history, code and design changes, bug database, software release and software evolution. To meet increased pressure of releasing updated or new versions of software systems due to changing requirements of stakeholder, software are rarely built from scratch. Software reusability is a primary attribute of software quality which aims to create new software systems with a likelihood of using existing software components to add, modify or delete functionalities in order to adapt to new requirements imposed by stakeholders. Software reuse using software components or modules provide a vehicle for planning and re-using already built software components efficiently. In this paper, we propose a framework for our approach to predict software reusable components from existing software repository on the basis of (1) stakeholders intention (requirement) match and (2) similarity index count for better reuse prediction. To effectively manage storage and retrieval of relevant information we use concept of situational method engineering to match and analyze the information for reuse. We use Genetic algorithm, Rabin Karp algorithm for feature selection and classification and k-means clustering methods of data mining to refine our results of prediction in order to better manage and produce high quality software systems within estimated time and cost.