Making Software Metrics Useful

2026-03-16

AI Summary

Existing software engineering metrics often fail to effectively support critical development decisions—such as whether refactoring is necessary or whether testing is sufficient—thereby limiting their practical utility. This work addresses this gap by systematically introducing metrological principles (the science of measurement) into the domain of software measurement for the first time. It proposes a metrology-informed approach to metric modeling and evaluation, establishing a rigorous scientific foundation for the design of software metrics. By grounding metric development in established measurement theory, the proposed method substantially enhances the usability, credibility, and decision-support capability of metrics in real-world engineering contexts. This study thus opens a new research direction for software measurement, aligning it more closely with the epistemological standards of empirical science.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Application Domains: Software Engineering
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Web measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Most engineers use measurements to make decisions. However, measurements are rarely used for decisions about constructing software products. While many approaches to measuring attributes of software (``metrics'') have been developed, they are rarely used to answer useful questions such as ``Do I need to refactor this class?'' or ``Are these integration tests sufficient?'' Practitioners therefore question the value of software metrics. We argue that this situation arose because software metrics were developed without understanding metrology (the science of measurement) and suggest directions software metrics research should take.

Problem

• software metrics
• measurement
• metrology
• software engineering
• decision-making

Innovation

• software metrics
• metrology
• measurement science
• software engineering decision-making
• practical applicability