Today, the demand for security software is Six Sigma quality, i.e. practically zero-defects. A practical and stochastic method is proposed for a Six Sigma security software quality management. Monte Carlo Simulation i...Today, the demand for security software is Six Sigma quality, i.e. practically zero-defects. A practical and stochastic method is proposed for a Six Sigma security software quality management. Monte Carlo Simulation is used in a Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) approach to security software testing. This elaboration used a published real project’s data from the final product testing lasted for 15 weeks, after which the product was delivered. The experiment utilised the first 12 weeks’ data to allow the results verification on the actual data from the last three weeks. A hypothetical testing project was applied, supposed to be completed in 15 weeks. The product due-date was Week 16 with zero-defects quality assurance aim. The testing project was analysed at the end of the 12th week with three weeks of testing remaining. Running a Monte Carlo Simulation with data from the first 12 weeks produced results which indicated that the product would not be able to meet its due-date with the desired zero-defects quality. To quantify an improvement, another simulation was run to find when zero-defects would be achieved. Simulation predicted that zero-defects would be achieved in week 35 with 56% probability, and there would be 82 defects from Weeks 16 - 35. Therefore, to meet the quality goals, either more resources should be allocated to the project, or the deadline for the project should be moved to Week 36. The paper concluded that utilising Monte Carlo Simulations in a Six Sigma DMAIC structured framework is better than conventional approaches using static analysis methods. When the simulation results were compared to the actual data, it was found to be accurate within ﹣3.5% to +1.3%. This approach helps to improve software quality and achieve the zero-defects quality assurance goal, while assigning quality confidence levels to scheduled product releases.展开更多
文摘Today, the demand for security software is Six Sigma quality, i.e. practically zero-defects. A practical and stochastic method is proposed for a Six Sigma security software quality management. Monte Carlo Simulation is used in a Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) approach to security software testing. This elaboration used a published real project’s data from the final product testing lasted for 15 weeks, after which the product was delivered. The experiment utilised the first 12 weeks’ data to allow the results verification on the actual data from the last three weeks. A hypothetical testing project was applied, supposed to be completed in 15 weeks. The product due-date was Week 16 with zero-defects quality assurance aim. The testing project was analysed at the end of the 12th week with three weeks of testing remaining. Running a Monte Carlo Simulation with data from the first 12 weeks produced results which indicated that the product would not be able to meet its due-date with the desired zero-defects quality. To quantify an improvement, another simulation was run to find when zero-defects would be achieved. Simulation predicted that zero-defects would be achieved in week 35 with 56% probability, and there would be 82 defects from Weeks 16 - 35. Therefore, to meet the quality goals, either more resources should be allocated to the project, or the deadline for the project should be moved to Week 36. The paper concluded that utilising Monte Carlo Simulations in a Six Sigma DMAIC structured framework is better than conventional approaches using static analysis methods. When the simulation results were compared to the actual data, it was found to be accurate within ﹣3.5% to +1.3%. This approach helps to improve software quality and achieve the zero-defects quality assurance goal, while assigning quality confidence levels to scheduled product releases.
