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Many empirical studies have found that software metrics can predict class error proneness and the prediction can be used to accurately group error-prone classes.
Whether software metrics can still predict class error proneness in a system's post-release evolution is still a question to be answered.
These studies, however, focused on the relationship between software metrics and class error proneness during the development phase of software projects.
This study examined three releases of the Eclipse project and found that although some metrics can still predict class error proneness in three error-severity categories, the accuracy of the prediction decreased from release to release.
Although there is a plethora of empirical studies linking software metrics to errors and error proneness of classes in object-oriented systems, the link between the bad smells and class error probability in the evolution of object-oriented systems after the systems are released has not been explored.
Moreover, unlike other investigations, our study focussed on the handwritten medication documentation and its error proneness.
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This results in increased error-proneness, higher development effort, misinterpretations and miscalculations.
A literature review was conducted to extract factors that may differentiate people with respect to human error-proneness.
Conclusions were drawn from this evaluation regarding other quality attributes correlated with these two – stability, changeability, error-proneness, and reusability.
However, new design challenges emerge due to the error-proneness, self-organization and mobility nature of these networks.
Recent research has developed methods to reduce this error-proneness by introducing a new way of object-oriented modeling of spreadsheets before using them.
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CEO of Professional Science Editing for Scientists @ prosciediting.com