and other version control tasks. As a distributed revision control system it is aimed at speed, data integrity, and support for distributed, non-linear workflows https://try.github.io/levels/1/challenges/1

Yet the DoD has not yet accumulated enough experience with Agile approaches to permit rigorous analysis of strategies, methods, and outcomes. Given this lack of well-documented research and of historical small-medium-large as units for assigning story points. Over time, as the teams accumulate performance data, this iterative and incremental4 process improves accuracy in allocating points. Point values are optimized for weapon systems acquisition. These traditional methods most often involve extensive analysis, planning, and documentation, as well as large-scale reviews and milestones that ensure readiness

problem-solving. ii. Telemetry – An automated communications process by which measurements and other data are collected at remote points and are subsequently transmitted to receiving equipment for monitoring development. Operations don’t just monitor what’s up or down. ii. Modern Monitoring architecture 1. Data Collection at business logic, application, & environments layer a. Events, logs, & metrics b. Common threshold-based alerting & health checks iii. Transform logs/events into metrics to enable statistical analysis iv. Adrian Cockcraft – “Monitoring is so important that our monitoring systems need to be more

Testing standards and tools, including security 4. Deployment pipeline tools 5. Monitoring and analysis tools 6. Tutorials and standards ii. Google – Single repository with over 1B files and over 2B automated tests 1. Static Analysis – inspect program code for all possible run-time behaviors and seek out coding flaws, back doors, and potentially malicious code2. Dynamic Analysis – tests executed while known vulnerabilities and consolidate multiple versions of the same library iii. 2014 Verizon PCI Data Breach Investigation Report – studies over 85K cardholder breaches. 10 vulnerabilities accounted

proceeds the data • Put the data in • Fast to use • Can lose subtle contexts • Good for exploitation; not for exploration & change Sense-making Frameworks Sense-making framework - the data proceeds proceeds the framework • Capture the data • Patterns emerge from the data • Provides context and awareness • Good for non-trivial domainsObvious • Cause & Effect Relationships exist • Relationships failure of process Complicated - deep experts see failures because didn’t have enough time or analysis Complex - politicians, battlefield commanders will get lots of people from lots of backgrounds

Operations to improve outcomes 2. Ch. 9 – Create the Foundations of Our Deployment Pipeline a. Enterprise Data Warehouse program by Em Campbell-Pretty - $200M, All streams of work were significantly behind schedule Application code & dependencies 2. Environment scripts & creation tools 3. DB scripts and reference data 4. Containers 5. Automated tests 6. Project artifacts – documentation, procedures, etc. 7. Application 1. Commit stage – build & package software, runs automated unit tests, and code quality (static analysis, coverage, style, etc.) 2. Package the code only once – use the same packages throughout the pipeline

accurately assess probabilities. Traditional teams attempt to drive out uncertainty by planning and analysis. Agile teams tend to drive out uncertainty by developing working software in small increments and stewarding three critical assets: the Enterprise Architecture asset, the IT people asset, and the data asset. These three assets represent the capabilities of the company and its ability to address the

signature-based continuous scanning using Common Vulnerabilities and Exposures (CVEs), runtime behavior analysis, and container policy enforcement.The security stack in the security sidecar container will include: Retrieval Service 2. Service Mesh 3. Program-specific artifact repository 4. Runtime Behavior Analysis Artificial Intelligence (AI) service 5. DCAR for the hardened containers 6. Common Vulnerabilities

transformation but requires substantial changes in behavior at every level, and Mark’s thorough analysis will prove invaluable to leaders who must execute it.” – Jez Humble, CTO, DevOps Research & Assessment

architectures, demonstrated alignment of systems with business needs, and did the “rigorous” up-front analysis and centralized planning that could then be used to set boundaries for developers when they began