Software analysis and prediction system development is the significant and much-needed field of software testing in software engineering. The automatic software predictors analyze, predict, and classify a variety of errors, faults, and defects using different learning-based methods. Many research contributions have evolved in this direction. In recent years, however, they have faced the challenges of software validation, non-balanced and unequal data, classifier selection, code size, code dependence, resources, accuracy, and performance. There is, therefore, a great need for an effective and automated software defect-based prediction system that uses machine learning techniques, with great efficiency. In this paper, a variety of such studies and systems are discussed and compared. Their measurement methods such as metrics, features, parameters, classifiers, accuracy, and data sets are found and discriminated. In addition to this, their challenges, threats, and limitations are also stated to demonstrate their system’s effectiveness. Therefore, it was discovered that such systems accounted for 44% use of the NASA’s PROMISE data samples, 68.18% metrics use of software, and also 16% use of the Logistic Regression method.