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Identifying defective software modules is a major issue of concern in the software industry which facilitates further software evolution and maintenance. Early prediction of defective software modules helps the software project manager to effectively utilize the resources such as people, time, and budget to develop high quality software. Developing a defect-free software product is a very challenging task due to the occurrence of unknown bugs or unforeseen deficiencies even if all the guidelines of software project development are followed carefully. These phases should be operated effectively in order to deliver bug-free and high quality software product to the end users. Software Development Life Cycle (SDLC) consists of five phases: Analysis, Design, Implementation, Test, and Maintenance phases. The significance of the results has been tested via statistical analysis performed by using nonparametric Wilcoxon signed rank test. The experiments on eight software defect prediction datasets have proved the validity of the proposed defect prediction system. This system assigns higher misclassification cost to the data samples of defective classes and lower cost to the data samples of nondefective classes. Therefore, on considering the misclassification cost issue, we have developed a software defect prediction system using Weighted Least Squares Twin Support Vector Machine (WLSTSVM). Misclassification cost of defective software modules generally incurs much higher cost than the misclassification of nondefective one. The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The early prediction of defective software modules can help the software developers to allocate the available resources to deliver high quality software products. Software defect predictors are useful to maintain the high quality of software products effectively.