An effective data collection methodology for evaluating software development methodologies was applied to four different software development projects. Goals of the data collection included characterizing changes and errors, characterizing projects and programmers, identifying effective error detection and correction techniques, and investigating ripple effects. The data collected consisted of changes (including error corrections) made to the software after code was written and baselined, but before testing began. Data collection and validation were concurrent with software development. Changes reported were verified by interviews with programmers
The evaluations of and recommendations for the use of software development measures based on the pra...
The use of dynamic characteristics as predictors for software development was studied. It was found ...
For 15 years, the Software Engineering Laboratory (SEL) at GSFC has been carrying out studies and ex...
Error data obtained from two different software development environments are compared. To obtain dat...
The Software Engineering Laboratory was monitoring software development at NASA Goddard Space Flight...
A set of quantitative approaches is presented for evaluating software development methods and tools....
Software development predictors, error analysis, reliability models and software metric analysis are...
The conceptual model, the data classification scheme, and the analytic procedures are explained. The...
Results are reported from a series of investigations into the effectiveness of various methods and t...
NASA's environment mirrors the changes taking place in the nation at large, i.e. workers are being a...
The Software Engineering Laboratory, software tools, software errors and cost estimation are address...
A detailed description of the data analyzed including definitions of measures, lists of values, and ...
Since 1976, the Software Engineering Laboratory (SEL) has been dedicated to understanding and improv...
An extensive series of studies of software design measures conducted by the Software Engineering Lab...
The distributions and relationships derived from the change data collected during the development of...
The evaluations of and recommendations for the use of software development measures based on the pra...
The use of dynamic characteristics as predictors for software development was studied. It was found ...
For 15 years, the Software Engineering Laboratory (SEL) at GSFC has been carrying out studies and ex...
Error data obtained from two different software development environments are compared. To obtain dat...
The Software Engineering Laboratory was monitoring software development at NASA Goddard Space Flight...
A set of quantitative approaches is presented for evaluating software development methods and tools....
Software development predictors, error analysis, reliability models and software metric analysis are...
The conceptual model, the data classification scheme, and the analytic procedures are explained. The...
Results are reported from a series of investigations into the effectiveness of various methods and t...
NASA's environment mirrors the changes taking place in the nation at large, i.e. workers are being a...
The Software Engineering Laboratory, software tools, software errors and cost estimation are address...
A detailed description of the data analyzed including definitions of measures, lists of values, and ...
Since 1976, the Software Engineering Laboratory (SEL) has been dedicated to understanding and improv...
An extensive series of studies of software design measures conducted by the Software Engineering Lab...
The distributions and relationships derived from the change data collected during the development of...
The evaluations of and recommendations for the use of software development measures based on the pra...
The use of dynamic characteristics as predictors for software development was studied. It was found ...
For 15 years, the Software Engineering Laboratory (SEL) at GSFC has been carrying out studies and ex...