Feature driven development (FDD) is an agile process model that develops software according to the client features. The FDD consists of five processes, several practices and both are providing benefits to improve the software development. Although the FDD provides lots of benefits, but still endures many flaws. In previous research, there have been made numerous modifications in FDD with different aspects. These modifications could not fix all type of flaws and FDD requires improvements in many aspects. These flaws reduce the agility to deliver increments continuously and make an inverse relationship between quality and agility. Due to this relationship, the FDD does not utilize enough time on making extensive documentation, robust design, client or user involvement, and efficient testing. To overcome these issues, an enhanced feature driven development model is proposed. EFDD introduces best practice of agile manifesto named as behavioral driven development used in FDD. In this way, the focus on delivering increments quickly is achieved without affecting the quality of the software. The proposed model provides maximum agility with continuous delivery according to client features and efficient testing strategy which have asses every feature according to client specified functionality.

Testing is considered as one of the expensive activities in software development process. Fixing the defects during testing process can increase the cost as well as the completion time of the project. Cost of testing process can be reduced by identifying the defective modules during the development (before testing) stage. This process is known as “Software Defect Prediction”, which has been widely focused by many researchers in the last two decades. This research proposes a classification framework for the prediction of defective modules using variant based ensemble learning and feature selection techniques. Variant selection activity identifies the best optimized versions of classification techniques so that their ensemble can achieve high performance whereas feature selection is performed to get rid of such features which do not participate in classification and become the cause of lower performance. The proposed framework is implemented on four cleaned NASA datasets from MDP repository and evaluated by using three performance measures, including: F-measure, Accuracy, and MCC. According to results, the proposed framework outperformed 10 widely used supervised classification techniques, including: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”.

Feature driven development (FDD) is a process oriented and client centric agile software development model which develops a software according to client valued features. Like other agile models it also has adaptive and incremental nature to implement required functionality in short iterations. FDD mainly focus on designing and building aspects of software development with more emphasis on quality. However less responsiveness to changing requirements, reliance on experienced staff and less appropriateness for small scale projects are the main problems. To overcome these problems a Simplified Feature Driven Development (SFDD) model is proposed in this paper. In SFDD we have modified the phases of classical FDD for small to medium scale projects that can handle changing requirements with small teams in efficient and effective manner.