Friday, July 13, 2012

Agile Process Performance Model supporting CMMI- Feature Stability Prediction

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The intention of a process performance model is to identify a quantitative understanding of the performance of  the sub-processes which in turn  helps in providing process performance data, baselines and models to quantitatively manage the organization's projects. Process Performance Models are used to represent past and current process performance and to predict future results of the process. At the first glance this looks like very data intensive and very difficult for the agile projects to maintain. In this post we would give some insight into how continuous integration system can support in building feature stability prediction model.

Feature Stability Assessment is one method / technique based on which the feature stability can be assessed and there can be some prediction done for the quality of the product.

When the test cases are being designed, the following naming convention can be used:
Each Test Suite can be further mapped to the test cases level.
In each build the suite and the test cases which are failing can be monitored closely. Also there can be a trend which can be brought out about the features which are defective in last few builds.


Feature Stability Assessment can be done at Build Level and at the release level to check the quality of the features being built over a period. This can help in better prediction of the quality of the features and decisions can be taken to focus on specific improvements at feature level. Based on these factors regression models can be built to predict the quality of a product effectively.

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