Section 3.1: Inform & Measure

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Introduction

IT engineers are often unable to measure the quality of their work completely. Numerous indicators, and metrics exist, including unit test code coverage, lines of code (LOC), and code complexity, but none of these provide a clear picture of "where we stand" in terms of quality. For instance, defect-based metrics can provide great insight into the quality and progress of a system of mediocre to low quality but will provide little information on a high-quality system.

Is it necessary to measure metrics such as the number of automated tests, test case coverage, and pass/fail rate in DevOps when the goal is to quickly determine whether a given release candidate bears an acceptable level of risk?
What further safeguards can we implement to ensure that the constant stream of updates does not damage the same user experience we are aiming to enhance?
How to deploy forensic governance of our software lifecycle?

This section discusses how artificial intelligence can help us make better decisions by enhancing quality gates and smart analytics.

In this section

Chapter 1: Smart quality gates

Chapter 2: From good metrics to smart analytics

Chapter 3: Code Quality Analytics

Chapter 4: Code analytics extened to impact based testing

Chapter 5: Predicting Change Risk with Reliability

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