Abstract
When we talk about data science in the context of software engineering, we often only consider static artifacts that are independent of (or not generated by) the execution of software, eg, source code, version history, bug reports, mailing lists, developer network, and organization structure. We seldom consider dynamic artifacts that are dependent on (or generated by) the execution of software, eg, execution logs, crash/core dumps, call stacks, and traffic logs. Specifically, we seldom consider dynamic artifacts to enable the use of data science to improve software engineering tasks such as coding, testing, and debugging (in contrast to improving post-deployment activities such as monitoring for service degradation or security attacks). So, here are few experience nuggets to convince you to consider dynamic artifacts to improve software engineering tasks.
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