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Last year I presented the Project Defect Model, a defect measurement model piloted in one large project. Meanwhile the model has been used in a multiple of projects, varying from small to large,......
Last year I presented the Project Defect Model, a defect measurement model piloted in one large project. Meanwhile the model has been used in a multiple of projects, varying from small to large, and from one delivery to multiple increments with various in between deliveries. This presentation shows how the model evolved, the benefits, and what we have learned on defect prevention.
To able to use the model in a broad set of projects, a template model was made, including parts that are applicable in most projects in a configurable way. Also a user guide, with industry reference data, and overview presentation has been made.
The model was used in several smaller projects with a single team. Main benefit was comprehensive defect information, used in release decisions. Also the model was used in several larger projects, some still ongoing. There the model provides more insight in the defect flows and process performance, supporting early quality risk detection and process improvement. Finally the model is used in several subprojects from one total project, making interdependencies clearer, enabling better planning and more reliable product release decisions.
The organization learned a lot while using the model. Initially focus was on earlier defect detection, e.g. function- iso system test and inspection iso test. With the increase of data, more insight is acquired in design and coding activities, providing means for defect prevention. Now that data is available from a larger set of projects, processes can be compared enabling decisions about best practices from one project that can be spread towards future projects. Frequent estimation & feedback sessions with the design & test teams show that it is initially difficult to provide reliable estimates, but defect data that is acquired during the project enables early action taking, and better defect estimates resulting in early release quality predictions.