SAFe DevOps Practitioner (SDP) 6.0

The %C&A metric measures the percent of time downstream customers receive work that is usable as-is in the Continuous Delivery Pipeline. The %C&A metric is a measure of the quality of the work output from a process step. It indicates the percentage of time that the downstream customers receive work that is acceptable as is, without any rework or errors. To calculate the %C&A metric, the people responsible for the next step need to inspect the work output from the previous step and determine whether it meets the quality standards and expectations. They also need to provide feedback to the previous step on the defects or issues they find and how they affect the value stream. By measuring the %C&A metric, the enterprise can gain insight into the current state of the delivery process, such as the lead time, cycle time, throughput, quality, and waste. This insight can help the enterprise identify bottlenecks, dependencies, handoffs, delays, and inefficiencies that affect the flow of value. The %C&A metric can also help the enterprise understand how the flow of value can be improved by applying the principles and practices of DevOps, such as culture, automation, lean flow, measurement, and recovery. By improving the %C&A metric, the enterprise can increase customer satisfaction, reduce costs, accelerate time-to-market, and enhance business agility

According to the SAFe DevOps Practitioner 6.0 study guide1, one of the benefits of DevOps is better alignment between strategy and execution. DevOps is a mindset, culture, and set of technical practices that supports the integration, automation, and collaboration needed to effectively develop and operate a solution. DevOps helps break down organizational silos and develop a Continuous Delivery Pipeline (CDP)
-- a high-performance innovation engine capable of delivering market-leading solutions at the speed of business. By aligning strategy and execution, DevOps enables teams to deliver value to customers faster and more reliably, as well as to adapt to changing market conditions and customer needs. Therefore, better alignment between strategy and execution is one of the benefits of DevOps.

According to the SAFe DevOps Practitioner 6.0 study guide1, hypothesis evaluation is important when analyzing data from monitoring systems in the Release on Demand aspect because it helps link objective production data to the hypothesis being tested. Hypothesis evaluation is the process of measuring and comparing the actual outcomes of a hypothesis with the expected outcomes based on customer feedback and business value. Hypothesis evaluation helps DevOps teams to validate or invalidate their hypotheses, learn from their mistakes, and improve their solutions. By linking objective production data to the hypothesis being tested, DevOps teams can see how well their solutions are delivering value to customers and achieving their goals.

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, specific improvements to the Continuous Delivery Pipeline are identified in Future-state Value Stream Mapping. This activity involves envisioning and designing the ideal state of the value stream, based on the current state and the desired outcomes. The handbook states that "Future-state Value Stream Mapping is a way of defining how you want your value stream to look like in the future."1 Therefore, Future-state Value Stream Mapping helps teams identify and prioritize the areas that need improvement in their Continuous Delivery Pipeline.

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, one potential outcome of the Verify activity of the Continuous Delivery Pipeline is to analyze regression testing results. This means that the Verify activity involves checking the functionality and performance of the Solution after a deployment, and comparing it with the expected results. The handbook states that "Verify is the activity of checking the functionality and performance of the Solution after a deployment, and comparing it with the expected results."1 Therefore, one of the possible outcomes of this activity is to analyze how well the Solution meets the quality standards and customer requirements.

The purpose of value stream mapping is to visualize how value flows from concept to cash and to deliver incremental value in the form of working, tested software and systems. Value stream mapping helps to identify the current state of the value stream, the steps, activities, roles, and tools involved, and the delays, waste, and inefficiencies that can be eliminated or improved. It also helps to create a future state vision and an action plan for continuous improvement

According to the SAFe DevOps Practitioner 6.0 study guide1, feature toggles are useful for decoupling deployment from release. Feature toggles are a technique that allows code to be turned "on" or "off" without needing additional deployment. Feature toggles enable teams to experiment with different features, validate their assumptions, and learn from their failures without affecting the rest of the system. Feature toggles also help teams to reduce the risk of deploying faulty or incomplete code, as well as to optimize the performance and resource consumption of the system. Therefore, feature toggles are useful for decoupling deployment from release, which is an aspect of the Continuous Delivery Pipeline that releases new functionality immediately or incrementally based on business and customer needs 2.

Weighted Shortest Job First (WSJF) is a prioritization model used to sequence work for maximum economic benefit. In SAFe, WSJF is estimated as the relative cost of delay divided by the relative job duration. In a flow-based system, priorities must be continuously updated to provide the best economic outcomes. In other words, job sequencing produces the best results rather than prioritization based on a theoretical return on investment for each job. To that end, SAFe applies WSJF to prioritize backlogs.
Backlogs are continuously prioritized based on a WSJF algorithm that uses relative user and business value, time criticality, risk reduction and/or opportunity enablement, and job size. WSJF also conveniently and automatically ignores sunk costs, a fundamental principle of Lean economics

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, test data management is one reason for test data management. This means that test data management helps teams ensure that the application under test has a consistent and reliable state, by automating the provisioning of data from various sources and formats. The handbook states that "Test data management is the process of automating provisioning of data to an application in order to set it to a known state before testing begins."1
Therefore, test data management enables teams to perform end-to-end testing with confidence and accuracy.

According to the SAFe DevOps Practitioner 6.0 study guide1, scanning application code for security vulnerabilities is an important step in the Continuous Integration aspect of the Continuous Delivery Pipeline. Continuous Integration is the process of developing, testing, integrating, and validating new functionality in preparation for deployment and release. Continuous Integration helps teams to improve quality, reduce risk, and establish a fast, reliable, and sustainable development pace. Scanning application code for security vulnerabilities is one of the ways to ensure that the code meets the quality standards and requirements, and that it does not contain any errors, bugs, or vulnerabilities that could compromise the security or functionality of the Solution. Scanning application code for security vulnerabilities can be performed by using tools such as GitHub Advanced Security for Azure DevOps2, which uses CodeQL to identify vulnerabilities in various languages and frameworks. Therefore, scanning application code for security vulnerabilities is an important step in the Continuous Integration aspect of the Continuous Delivery Pipeline.