In other words, an improved design can lead to benefits for subsequent phases of software development and, therefore, a more efficient software life cycle. To aid in the search for better methods of software engineering, the Design Metrics Research Team at Ball State University is analyzing university and industry software to be able to correctly identify error-prone modules during the design phase of software development. This tool improves the metrics collection by ensuring the accuracy of metrics calculation and by minimizing the interference with the existing work load of a software engineer. This paper highlights the features of the DMA and provides some insight into its development. Metric monsters are stumbling blocks that prevent software metrics-guided methodologies from attaining product and process improvement.
Metric monsters can occur during the identification, collection or application of software metrics.
Based on this research, we will identify some of the monsters that occur when we try to support quantitative analyses of software and its development processes, and offer battle plans to slay them. We also present a metrics model consisting of tools, guidelines and actions for the application of software metrics. Design Metrics Research Team at Ball State University has developed a metrics approach for analyzing software designs that helps designers engineer quality into the design product. Two of the design metrics developed by the team are an external design metric De, which focuses on a module's external relationships to other modules in the software system, and an internal design metric Di, which incorporates factors related to a module's internal structure. However, on a system from Harris Corporation, the metrics seemed to perform poorly until further investigation revealed the reasons.