ODC - Orthogonal Defect Classification

Key Concepts in ODC

ODC essentially means that we categorize a defect into classes that collectively point to the part of the process which needs attention, much like characterizing a point in a Cartesian system of orthogonal axes by its (x, y, z) coordinates. Although activities are broadly divided into design, code and test, in the software developemnt process, each organization can have its variations. It is also the case that the process stages in several instances may overlap while different releases may be developed in parallel. Process stages can be carried out by different people and sometimes different organizations. Therefore, for classification to be widely applicable, the classification scheme must have consistency between the stages. Without consistency it is almost impossible to look at trends across stages. Ideally, the classification should also be quite independent of the specifics of a product or organization. If the classification is both consistent across phases and independent of the product, it tends to be fairly process invariant and can eventually yield relationships and models that are very useful. Thus, a good measurement system which allows learning from experience and provides a means of communicating experiences between projects has at least three requirements:

• orthogonality,
• consistency across phases, and
• uniformity across products.

One of the pitfalls in classifying defects is that it is a human process, and is subject to the usual problems of human error, confusion, and a general distaste if the use of the data is not well understood. However, each of these concerns can be handled if the classification process is simple, with little room for confusion or possibility of mistakes, and if the data can be easily interpreted. If the number of classes is small, there is a greater chance that the human mind can accurately resolve between them. Having a small set to choose from makes classification easier and less error prone. When orthogonal, the choices should also be uniquely identified and easily classified.

• Distribution change as a measurement