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Browsing by Author "Canning, James"

Now showing 1 - 3 of 3
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    Applying Structure and Code Metrics to Three Large-Scale Systems
    Kafura, Dennis G.; Canning, James (Department of Computer Science, Virginia Polytechnic Institute & State University, 1985)
    This work extends the area of research termed software metrics by applying measures of system structure and measures of system code to three realistic software products. Previous research in this area has typically been limited to the application of code metrics such as : lines of code, McCabe's Cyclomatic number, and Halstead's software science variables. However, this research also investigates the relationship of four structure metrics: Henry's Information Flow measure, Woodfield's Syntactic Interconnection Model, Yau and Collofello's Stability measure and McClure's Invocation complexity, to various observed measures of complexity such as, ERRORS, CHANGES and CODING TIME. These metrics are referred to as structure measures since they measure control flow and data flow interfaces between system components. Correlating the metrics to observed measures of complexity indicated that the Information Flow metric and the Invocation Measure typically performed as well as the three code metrics when project factors and subsystem factors were taken into consideration. However, it was generally true that no single metric was able to satisfactorily identify the variations in the data.
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    Exposing Useful Trends in Metric Data Through Group Level Analysis
    Kafura, Dennis G.; Canning, James (Department of Computer Science, Virginia Polytechnic Institute & State University, 1985)
    In this paper the results of experiments which applied both structure and code metrics to three large scale systems are presented. This metric research is distinct in that trends in the data are uncovered through the use of group level analysis. Components are partitioned into groups based on their various metric values and on observed measures of complexity (ie. errors, coding time). Crosstabulation data is given which indicates that trends between some of the metrics and the observed data do exist. Code metrics typically formed groups of increasing complexity which corresponded to increases in the mean values of the observed data. The strength of the Information Flow metric and the Invocation measure is their ability to form a group containing highly complex components which was found to be populated by outliers in the observed data.
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    Using Group and Subsystem Level Analysis to Validate Software Metrics on Commercial Software Systems
    Kafura, Dennis G.; Canning, James (Department of Computer Science, Virginia Polytechnic Institute & State University, 1988)
    This paper reports the results of a study which examined the relationship between a collection of software metrics and the development data (such as errors and coding time) of three commercially produced software systems. The software metrics include both measures of system interconnectivity and measures of system code. This study revealed strong relationships between the metrics and the development data when individual components were aggregated by structure (into subsystems) or by similarity (into groups). The subsystem and group results imply that research and application of metrics should be focused above the component level. The group results also imply that metrics can guide the effective application of project resources by identifying those groups which, for example, will contain a disproportionately large fraction of errors. Finally, the study showed the overall utility of two interconnectivity metrics: Henry and Kafura's information flow metric and McClure's invocation metric. This result is significant because interconnectivity metrics can be applied early in the life cycle.