"Orthogonal Defect Classification Using Defect Data to Improve Software Development”
Traditionally, defects represent the undesirable aspects of software’s quality. Root Cause Analysis (RCA) and Statistical Growth Modeling (e.g. S-curves) have played useful roles in the analysis of software defects. Effective RCA, while yielding exhaustive details on each defect, takes substantial investment of resources for completion and points to too many actions as a result. Growth modeling, on the other hand, provides an easy way to monitor trends, but is not capable of suggesting corrective actions due to the inadequate capture of the semantics behind the defects.
ODC is a scheme to capture the semantics of each software defect quickly. It is the definition and capture of defect attributes that make mathematical analysis and modeling possible. Analysis of ODC data provides a valuable diagnostics method for evaluating the various phases of the software life cycle (design, development, test and service) and the maturity of the product. This is much like the diagnostics done in medicine using the blood sample from a patient to understand the existing health conditions and arrive at corrective actions. ODC makes it possible to push the understanding and use of defects well beyond quality. |
