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One of the software engineering interests is quality assurance activities such as testing, verification and validation, fault tolerance and fault prediction. When any company does not have sufficient budget and time for testing the entire application, a project manager can use some fault prediction algorithms to identify the parts of the system that are more defect prone. There are so many prediction approaches in the field of software engineering such as test effort, security and cost prediction. Since most of them do not have a stable model, software fault prediction has been studied in this paper based on different machine learning techniques such as decision trees, decision tables, random forest, neural network, Naïve Bayes and distinctive classifiers of artificial immune systems (AISs) such as artificial immune recognition system, CLONALG and Immunos. We use four public NASA datasets to perform our …