Over 87% of software teams now leverage agile practices to deliver higher quality code in ever-shortening cycles. But maximizing productivity, reducing risk, and optimizing velocity across continuously evolving CI/CD ecosystems creates formidable management challenges.
Traditional analytics provides only surface-level visibility by capturing summary metrics on cost, quality and velocity for builds, commits or tests. While useful, these generalized averages mask the detailed realities within complex, distributed development workflows and systems.
Process mining overcomes these limitations to unlock entirely new speed and quality dimensions for software creation and delivery…
How Process Mining Uncovers Development Process Insights
Process mining extracts detailed event data about software development activities – such as commits, builds, tests, deployments – from systems like Jira, GitHub, Jenkins, and IDEs. Sophisticated algorithms correlate and sequence this event data to automatically discover, monitor, and analyze processes with unprecedented precision.
Unlike interviews, surveys or guesswork, process mining provides an objective picture into development reality based on systems data.
There are three main types of insights process mining delivers:
Process discovery – Illustrates the end-to-end development workflow with diagrams showing activities, decisions, variants, performance metrics, and roles. This helps teams visualize the actual process versus assumptions.
Conformance checking – Highlights deviations by comparing event logs to preset process models and rules. This reveals inefficiencies, risks, bottlenecks.
Enhancement – Drills down into variants, metrics, and data aspects not visible in diagrams. This connects insights to process improvement.
Let‘s look at some specific use cases where software teams employ process mining to significant benefit.
7 Process Mining Use Cases for Software Development Teams
1. Map Software Delivery Lifecycle Stages
The software development lifecycle (SDLC) typically involves stages like planning, analysis, design, coding, testing, release and maintenance. While methodologies provide a high-level view of SDLC phases, they don‘t show granular decisions and activities.
With detailed event data, process mining gives transparency into how work flows through each SDLC stage in reality – exposing hidden waste and delays.
This helps managers ensure critical steps aren‘t skipped and optimize cycle times.
For example, by processing Jira issue transitions, source code check-ins, build logs, and test case executions, one game developer mapped requirement-to-release processes across their agile Scrum workflow.
This end-to-end visualization exposed significant inefficiencies within their development testing practices – prompting a redesign that reduced escaped defects by 18%.
[Insert process map diagram showing SDLC stages]2. Monitor Project Progress and Health
Trying to manually gauge progress across multiple, concurrent software projects is nearly impossible…
