Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 46, Pages: 1-5
Supriya V. Sullad and A. Pauline
Objectives: The defect detection efficiency has to be improved by comparing different software development lifecycles and finding the best of defect detection methodology, along with the accurate defect rate analysis and classification and to achieve 100% efficiency in defect detection and attain 100% customer satisfaction. Methods and Statistical Analysis:- Agile methodology, with In-memory analytics is employed to improve the effectiveness of defect detection efficiency. Along with Agile methodology, defect comparison and classification of defects based on defect rate with respect to the standard threshold values, at each stage of the design process can be employed. In memory analytics can be employed for Defect classification. This method provides effectiveness of defect classification and rates defect as low, high and medium. This method easier the tasks of designer to detect the severity of defect and rectify, to avoid the defect being added to subsequent phases. Findings: It is found that, In Waterfall model, the product is tested only after the product has been completely manufactured. The defect detection effectiveness considering an average of 15-20 percentages of defects originating at each phase is 50 percent. In Six Sigma approach, the defect detection effectiveness which is improved to 99.9997 percent with the same percentage of defect originating at each phase. While 100 percent defect detection effectiveness is not practically possible. Hence the greatest challenge is how the testing engineers can meet 100 percent testing standard. The testing engineers need to adapt a unique technique to remove the defects before they get added to the system. Such technique will not only helps to detect defects faster but also reduces the high cost of poor quality products.
Keywords: Defect Detection Methodology with Accurate Data Analytics, Defect Detection and Classification, Defect Analysis, Defect Characterization, In-Memory Analytics
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