The potential benefits of using DePress (Defect Prediction in Software Systems) Extensible Framework tool for commercial software development projects have not been previously investigated. Documented cases of machine-learning usage in industrial applications for defect prediction purposes, are rare. Due to these facts, representatives of Wroclaw University of Technology and Volvo Group began a long-term cooperation for that purpose. As achieving a positive result of research described in this paper could trigger more interest from business stakeholders, we decided to verify the cost effectiveness of DePress framework usage for defect prediction purposes and investigate, if the defect prediction technique would positively impact software development projects by generating profits. To meet this goal, we proposed using a defect prediction-based quality assurance (QA) effort allocation strategy based on the Pareto principle and Boehm’s law. Then, based on real life data collected from an actual, industrial software development project using DePress Extensible Framework, we conducted a defect prediction and simulated potential quality assurance costs based on the best prediction result and proposed QA strategy. Results of the simulation have been optimistic and have resulted in continued usage of DePress based defect prediction for actual industrial projects run by Volvo Group.