Designing a Personal Quality Score Model for Software Engineers

1. Personal Quality Score Model

The primary goal is to evaluate a developer's delivery efficiency together with delivery quality, using DevOps data as the source. The model balances several relationships:

Defect count vs. code volume

CR (code‑review) participation vs. effective CRs

Number of releases a developer is responsible for vs. rollback count

Ticket handling volume vs. SLA compliance

1.1 Quality Score Indicators

All data are collected from DevOps; abnormal scenarios can be excluded from the statistics. Specific score values are derived from historical data and then adjusted with appropriate coefficients.

Defect Basic Indicators

Defect count : total submitted defects (including all valid defects) – sourced from DevOps by the final defect owner.

Low‑level defects : smoke‑test failures, unmet requirements, mismatched design vs. implementation, obvious performance problems.

Reopen defects : defects marked Fixed that fail regression, Closed defects that reappear, and reopen actions performed by the defect submitter.

CR Basic Indicators

CR initiation count : number of CR processes started (DevOps).

CR participation count : number of CRs a developer participated in (DevOps).

Commit count : number of code commits (Git, DevOps).

Commit lines : sum of added, modified, and deleted lines.

CR‑covered lines : lines changed by CRs (add + modify + delete).

Comment count : number of comments submitted during CR participation.

Release Basic Data

Release count : number of releases for the application within the time window.

Rollback count : number of rollbacks performed during gray, beta, or production releases.

Emergency release count : number of releases marked as emergency.

Fault and Ticket Data

Fault count : faults directly caused by the developer (weighted by severity).

Ticket count : tickets assigned to the developer due to code changes.

Derived Measurement Metrics

kLOC defect rate : total defects ÷ (commit lines / 1000).

Low‑level defect rate : low‑level defects ÷ total defects.

Reopen defect rate : reopen defects ÷ total defects.

CR rate : CR initiations ÷ commit count (ideal value close to 100%).

Code CR rate : CR‑covered lines ÷ commit lines.

kLOC comment rate : comment count ÷ (commit lines / 1000).

Effective CR coefficient : comment count ÷ CR participation count.

Rollback rate : rollback count ÷ release count.

Emergency release rate : emergency releases ÷ total releases.

Responsibility CR count : CRs that caused faults or rollbacks, weighted by fault severity.

Code Quality Score

The calculation combines the above metrics with predefined weights (e.g., low‑level defect rate weight = 5, performance issue weight = 10, rollback weight = 20). Historical data are used for trial calculations, after which coefficients are finalized.

Potential Issues with the Code Quality Score

Whether the weight of the quality score should be increased to address left‑shift quality problems.

Whether rollbacks should receive different weights based on the criticality of the application.

How teams will use the quality score for ongoing evaluation and improvement.

CR Contribution Rewards

Reward per CR participation.

Reward per comment submitted during a CR.

Effective CR coefficient (comments / participations) influences the final reward.

2. Application‑Level Quality Score

Additional dimensions include abnormal logs and slow SQL queries.

3. Team‑Level Quality Score

Additional dimensions include online issues, faults, monitoring effectiveness, and customer complaints.