Actionable software metrics: An industrial perspective
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
European Commission's projectQ-RAPIDS - Quality-Aware Rapid Software Development (EC-H2020-732253)
Background: Practitioners would like to take action based on software metrics, as long as they find them reliable. Existing literature explores how metrics can be made reliable, but remains unclear if there are other conditions necessary for a metric to be actionable. Context & Method: In the context of a European H2020 Project, we conducted a multiple case study to study metrics’ use in four companies, and identified instances where these metrics influenced actions. We used an online questionnaire to enquire about the project participants’ views on actionable metrics. Next ,we invited one participant from each company to elaborate on the identified metrics’ use for taking actions and the questionnaire responses (N=17). Result:We learned that a metric that is practical, contextual, and exhibits high data quality characteristics is actionable. Even a non-actionable metric can be useful, but an actionable metric mostly requires interpretation. However, the more these metrics are simple and reflect the software development context accurately, the less interpretation required to infer actionable information from the metric. Company size and project characteristics can also influence the type of metric that can be actionable. Conclusion: This exploration of industry’s views on actionable metrics help characterize actionable metrics in practical terms. This awareness of what characteristics constitute an actionablemetric can facilitate theirdefinition and developmentright from the start of a software metrics program
CitationRam, P. [et al.]. Actionable software metrics: An industrial perspective. A: International Conference on Evaluation and Assessment in Software Engineering. "Proceedings of EASE 2020, Evaluation and Assessment in Software Engineering: April 15-17, 2020, Norwegian University of Science and Technology, Norway". New York: Association for Computing Machinery (ACM), 2020, p. 240-249. ISBN 978-1-4503-7731-7. DOI 10.1145/3383219.3383244.
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