Simple detection of bugs, defects or anomalies during software development is not enough - it is necessary to apply corrective actions to eliminate them. To find out whether an anomaly exists in any software, we can measure the quality attributes using software metrics. The main goal of this paper was to find out and explain how to meaningfully attribute metrics to useful corrective actions.

This work addresses the problem of metric diversity in meta-analysis for Software Engineering by clustering studies using input-output tables and by vote-counting. Diversity arises when researchers, measuring same phenomena, use different, and typically "incomparable" metrics making impossible a direct analysis of the effects and their sizes. Additionally, this work discusses an application of proposed approach to the case of Software Reuse.