Software engineering (3rd ed.): a practitioner's approach
Software engineering (3rd ed.): a practitioner's approach
Taguchi's parameter design: a panel discussion
Technometrics
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
IEEE Transactions on Software Engineering
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This paper presents a methodology for selection of optimal software design parameters using the experimental design. When an organization is at the point of taking up a new project with an objective of improving the software quality, Taguchi method is applied for the software design process with an objective that not more than one error is found per software module. The strategy in robust design is to conduct off-line experiments using orthogonal arrays (OA) and to optimize the design by maximizing performance measures with respect to design parameters. Towards this a cause and effect diagrams for design errors was drawn with opinions from customer, production, quality personnel and engineers. This diagram gave three most likely parameters as candidate for software design error, they are coupling, number of requirements per module and McCabe's cyclomatic complexity. It was planned to consider coupling parameter at two levels as low coupling at levelland high coupling at level 2, in case of number of requirements per module parameter three levels were considered they are one requirement per module at level 1, two requirements per module at level 2 and greater than two requirements per module as the level 3. In case of McCabe's complexity value 10 is set at level 3. The possible number of factorial experiment required be conducted for levels selected for the threee parameters is 18. The appropriate orthogonal array based on the guidelines of Taguchi is L9 that is nine experiments need to be conducted to find the optimal software design parameters.