Scaled Agile SAFe-DevOps Dumps

When preparing a DevOps backlog, prioritizing features using WSJF includes two factors: cost of delay and duration/job size. WSJF stands for Weighted Shortest Job First, which is a prioritization model used to sequence work for maximum economic benefit. WSJF is estimated as the relative cost of delay divided by the relative job duration. Cost of delay is the money lost by delaying or not doing a job for a specific time. It is a measure of the economic value of a job over time. Job duration is the time it takes to complete a job. Jobs that can deliver the most value in the shortest duration provide the best economic return. WSJF also considers other factors that contribute to the cost of delay, such as user and business value, time criticality, and risk reduction and/or opportunity enablement

Mapping the value stream helps accomplish two actions: to gain insight into organizational efficiency and to understand how the flow of value can be improved. A value stream is the series of steps that an organization uses to implement solutions that provide a continuous flow of value to a customer. Mapping the value stream involves identifying the steps, people, inputs, outputs, tools, and metrics involved in delivering value from concept to cash. By mapping the value stream, the organization 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 organization identify bottlenecks, dependencies, handoffs, delays, and inefficiencies that affect the flow of value. Mapping the value stream also helps the organization 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 flow of value, the organization can increase customer satisfaction, reduce costs, accelerate time-to-market, and enhance business agility

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.