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Bug Count per 1k Lines of Code

What is this metric?

Amount of bugs per 1,000 lines of code.

Why is it important?

  1. Defect drill-down analysis to inform the development of design and code review strategies and to improve the internal QA process
  2. Assist teams to locate projects/modules with higher defect severity and density, and clean up technical debts
  3. Identify good/to-be-improved practices that affect defect count or defect rate, to reduce the number of future defects

Which dashboard(s) does it exist in


How is it calculated?

The number of bugs divided by the total accumulated lines of code (additions + deletions) in the given data range.

Data sources required
  • issues collected from Jira, GitHub or TAPD.
  • commits collected from GitHub, GitLab or BitBucket.
Data Transformation Required

This metric relies on the 'type-bug' configuration in Jira, GitHub or TAPD's transformation rules while adding/editing a blueprint. This configuration tells DevLake what issues are bugs.

SQL Queries

If you want to measure the monthly trend of Bugs per 1k lines of code in the screenshot below, please run the following SQL in Grafana.

with _line_of_code as (
DATE_ADD(date(authored_date), INTERVAL -DAY(date(authored_date))+1 DAY) as time,
sum(additions + deletions) as line_count
message not like 'Merge%'
and $__timeFilter(authored_date)
group by 1

_bug_count as(
DATE_ADD(date(created_date), INTERVAL -DAY(date(created_date))+1 DAY) as time,
count(*) as bug_count
from issues i
type = 'Bug'
and $__timeFilter(created_date)
group by 1

_bug_count_per_1k_loc as(
1.0 * bc.bug_count / loc.line_count * 1000 as bug_count_per_1k_loc
_line_of_code loc
left join _bug_count bc on bc.time = loc.time
bc.bug_count is not null
and loc.line_count is not null
and loc.line_count != 0

date_format(time,'%M %Y') as month,
bug_count_per_1k_loc as 'Bug Count per 1000 Lines of Code'
from _bug_count_per_1k_loc
order by time;

How to improve?

  1. From the project or team dimension, observe the statistics on the total number of defects, the distribution of the number of defects in each severity level/type/owner, the cumulative trend of defects, and the change trend of the defect rate in thousands of lines, etc.
  2. From version cycle dimension, observe the statistics on the cumulative trend of the number of defects/defect rate, which can be used to determine whether the growth rate of defects is slowing down, showing a flat convergence trend, and is an important reference for judging the stability of software version quality
  3. From the time dimension, analyze the trend of the number of test defects, defect rate to locate the key items/key points
  4. Evaluate whether the software quality and test plan are reasonable by referring to CMMI standard values