Code quality
Complexity, defects, tests, and maintainability in one score.
ENGINEERING INTELLIGENCE
From engineering data to a decision
DevScore connects delivery, quality, AI contribution, and security in one explainable model.
DevScore Concept
Minimal noise: one development analytics platform to track quality, efficiency and code security with AI adoption scoring.
Business
- Savings estimate
- Dev benchmark vs competitors
- AI adoption score
CTO
- Efficiency = (velocity x quality) / risks
- AI coverage by repositories
- Leaders and lagging by flow
Security
- Security rating
- Secrets/vulnerabilities
- Commit anomalies
System status
Metrics collection from repositories, CI/CD and Sonar, KPI calculations and visual dashboards without overload.
AI AND DEVELOPMENT UNDER CONTROL
TOP-6 Business Tasks for DevScore
Core DevScore scenarios: team comparison, transparent metrics, and practical decision support for engineering leaders.
#01
Team Productivity Assessment
Objective contribution metrics for each developer, including commit cadence, code quality, and review throughput.
#02
Code AI-Dependency Measurement
Track how much code is AI-generated, measure output quality, and identify risks before they hit production.
#03
Technical Debt Detection
Continuously analyze debt and prioritize refactoring based on business impact and maintenance economics.
#04
Repository Security
Detect vulnerabilities, leaked secrets, and risky dependencies and include security in the overall team rating.
#05
Management Transparency
Provide CTO and product stakeholders with clear dashboards and reports tied to outcomes and ROI.
#06
AI Expert Bot
A persistent AI bot explains team metrics, answers leadership questions, and suggests concrete next actions.
OWNER
Owner: comparison and decision support
One view across teams shows who delivers consistently, where risks grow, and what action is needed next.
#01
Compare teams by shared metrics: speed, quality, release stability, and change cost.
#02
Understand deviations: where focus drops, debt grows, and delivery predictability weakens.
#03
Ask the external-expert AI bot why metrics dropped and what three actions to run next sprint.
CTO
CTO: technical transparency and quality control
Technical team comparison by delivery flow, code quality, and security without manual reporting.
#01
Compare teams by DORA metrics, review quality, and debt volume using one consistent model.
#02
Find degradation causes: CI/CD bottlenecks, quality loss, and maintenance cost growth.
#03
AI bot gives technical answers: why score changed and which engineering actions provide fastest impact.
MANAGERS
Managers: observability, focus, and growth
DevScore makes team effort visible and links day-to-day focus to measurable outcomes.
#01
Observe team dynamics: execution speed, quality trends, and blocker accumulation.
#02
Understand focus balance: product work, debt reduction, stabilization, and incidents.
#03
Evaluate growth: quarterly team and engineer progress vs previous periods.
SECURITY
Security: clear risk snapshot and total rating impact
Security status is tracked in plain terms and directly affects the overall team rating.
#01
Secrets snapshot: what was detected, what was fixed, and what remains critical.
#02
Vulnerability snapshot: remediation priorities and overdue security debt.
#03
Security is part of the total score alongside speed and quality.