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Improved prediction in Extreme Programming Projects using Bayesian Networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Hearty, Peter Stewart Fenton, Norman E. Marquez, David Neil, Martin |
| Copyright Year | 2006 |
| Abstract | -Causal models (Bayesian networks) have been used with some success to provide software managers with improved risk assessment and quality assurance methods. It is possible to provide more intuitive and accurate predictions of key project attributes such as effort and defects because they take account of causal (process) factors. To date these methods have largely been restricted to projects using traditional development approaches such as waterfall or spiral. Yet agile software development methods, such as Extreme Programming, are becoming increasingly popular alternatives and have just as great a need for accurate predictions and risk assessment. We present a novel Bayesian Net model that can provide improved effort predictions and risk assessment in an Extreme Programming environment. The model successfully reproduces real world characteristics of XP Project Velocity, is capable of considerable extension, and uses data to learn key parameters. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://paginaspersonales.deusto.es/cortazar/doctorado/articulos/2007/xp_ieee_tse_24nov06.pdf |
| Alternate Webpage(s) | http://www.eecs.qmul.ac.uk/~norman/papers/xp_ieee_tse_24nov06.pdf |
| Alternate Webpage(s) | https://www.eecs.qmul.ac.uk/~norman/papers/xp_ieee_tse_24nov06.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
