The advent of technology has opened unprecedented opportunities in health\ncare delivery system as the demand for intelligent and knowledge-based systems\nhas increased as modern medical practices become more knowledge-intensive.\nAs a result of this, there is greater need to investigate the pervasiveness of software\nfaults in Safety critical medical systems for proper diagnosis. The sheer\nvolume of code in these systems creates significant concerns about the quality\nof the software. The rate of untimely deaths nowadays is alarming partly due\nto the medical device used to carry out the diagnosis process. A safety-critical\nmedical (SCM) system is a complex system in which the malfunctioning of\nsoftware could result in death, injury of the patient or damage to the environment.\nThe malfunctioning of the software could be as a result of the inadequacy\nin software testing due to test suit problem or oracle problem. Testing a SCM\nsystem poses great challenges to software testers. One of these challenges is the\nneed to generate a limited number of test cases of a given regression test suite\nin a manner that does not compromise its defect detection ability. This paper\npresents a novel five-stage fault-based testing procedure for SCM, a model-based\napproach to generate test cases for differential diagnosis of Tuberculosis. We\nused Prime Path Coverage and Edge-Pair Coverage as coverage criteria to ensure\nmaximum coverage to identify feasible paths. We analyzed the proposed\ntesting procedure with the help of three metrics consisting of Fault Detection\nDensity, Fault Detection Effectiveness and Mutation Adequacy Score. We evaluated\nthe effectiveness of our testing procedure by running the suggested test\ncases on a sample historical data of tuberculosis patients. The experimental results\nshow that our developed testing procedure has some advantages such as\ncreating mutant graphs and Fuzzy Cognitive Map Engine while resolving the\nproblem of eliminating infeasible test cases for effective decision making.
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