Practical software metrics for project management and process improvement
Practical software metrics for project management and process improvement
Applied software measurement (2nd ed.): assuring productivity and quality
Applied software measurement (2nd ed.): assuring productivity and quality
Coverage measurement experience during function test
ICSE '93 Proceedings of the 15th international conference on Software Engineering
IEEE Transactions on Software Engineering
Software assessments, benchmarks, and best practices
Software assessments, benchmarks, and best practices
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
The Roi From Software Quality
Verification and validation of simulation models
WSC '05 Proceedings of the 37th conference on Winter simulation
Estimating Software Costs
First Steps towards Validating a Cost-Benefit Model of Reviews and Tests
IWSM/Metrikon/Mensura '08 Proceedings of the International Conferences on Software Process and Product Measurement
Cost-Optimisation of Analytical Software Quality Assurance: Models, Data, Case Studies
Cost-Optimisation of Analytical Software Quality Assurance: Models, Data, Case Studies
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Software project managers must schedule quality assurance activities. This is difficult because not enough information is available. Therefore, we developed and validated the quantitative model CoBe. It is based on detailed relationships and is quantified with historical data. It allows to decide which reviews and tests have to be conducted, how they are conducted, and how corrected defects are retested. The results are costs and benefits for quality assurance activities during development and after delivery. Results are given in terms of effort, time, and staff. They are summed up and weighted financially so that an optimal trade-off between costs and benefits can be found. The model is validated with real-world data: Detailed relationships and the complete model are validated with data from 21 student projects. A sensitivity analysis was conducted. CoBe was also validated with data of two industry projects. Overall, the model results are sufficiently accurate. But a calibration is necessary for applying the model in a specific environment. For this, only a few parameters must be set. Their values can be obtained from data that is available frequently from past projects.