Abstract
Predicting a defect inflow is important for project planning and monitoring purposes. For project planning purposes and for quality management purposes, an important measure is the trend of defect inflow in the project - i.e. how many defects are reported in a particular stage of the project. Predicting the defect inflow provides a mechanism of early notification whether the project is going to meet the set goals or not. In this paper we present and evaluate a method for predicting defect inflow for large software projects: a method for short-term predictions for up to three weeks in advance on a weekly basis. The contribution of this paper is the fact that our model is based on the data from project planning, status monitoring, and current trends of defect inflow and produces results applicable for large projects. The method is evaluated by comparing it to existing defect inflow prediction practices (e.g. expert estimations) at one of the large projects at Ericsson. The results show that the method provides more accurate predictions (in most cases) while decreasing the time required for constructing the predictions using current practices in the company.
Highlights
The number of defects in a software project has a significant impact on project performance and is an input to project planning
For project planning purposes and for quality management, an important measure is the trend of defect inflow in the project – i.e. how many defects are reported in a particular time
In this paper we presented the method for providing short term predictions of defect inflow in large software projects based on the data from project plans and the defect inflow itself from the past weeks of the projects
Summary
The number of defects in a software project has a significant impact on project performance and is an input to project planning. Defect inflow is one of the most important variables to monitor in large scale software projects It provides the management with a possibility of identifying whether a given project is not going to meet the set goals and to adjust the project plan, if needed. It allows the organization to optimize resource allocation for projects – e.g. when there is a large defect inflow, the organization needs to provide additional person-hours to keep the project on track (e.g. by ordering overtime). It is used in line with effort estimations methods (e.g. COCOMO/COCOMO II (Boehm, 2000))
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