Examples
Examples
Try the starting example
- JSON Unique defect observations [ID,test-day index]
- [["D1",1],["D2",1],["D3",2],["D4",3]]
- Observed discovery curve [day,new,cumulative]
- [[1,2,2],[2,1,3],[3,1,4]]
- OLS discovery-count slope (defects/day²)
- -0.5
- OLS count intercept (defects/day)
- 2.333333
Try another scenario
- JSON Unique defect observations [ID,test-day index]
- [["D1",1],["D2",2],["D3",2],["D4",3],["D5",3]]
- Observed discovery curve [day,new,cumulative]
- [[1,1,1],[2,2,3],[3,2,5]]
- OLS discovery-count slope (defects/day²)
- 0.5
- OLS count intercept (defects/day)
- 0.666667
Calculation method
Calculation method
Observed discovery curve [day,new,cumulative]
Group unique defect IDs by supplied day and cumulatively sum counts.
OLS discovery-count slope (defects/day²)
Regress observed daily new-defect counts on observed day indices with equal day weight.
OLS count intercept (defects/day)
mean(count)−slope×mean(day); one distinct day gives slope zero.
Symbols and units
- a
- JSON Unique defect observations [ID,test-day index]
Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.
Assumptions & limits
Assumptions & limits
- Descriptive observed-day linear trend only. Untested days must not be treated as zero observations; no total-defect or release-readiness prediction. Supported limits: 1–1000 distinct defect IDs. One chosen build cohort per run.
Are these inputs private?
These calculations run in your browser. A shared link contains the values you choose to share. Only questions submitted to the separate AI finder are sent to OpenAI.