7. Current QA Challenges
● What all testcases need to be executed to minimize
the defect leakage rate < 10% and maximize the
coverage > 90%?
● Identify the tests to be included in test suite which can
be executed with resources <=5 and time_duration
<10 days with severity defects= 0% ? (Min(Tc))
● Number of resources required to execute test suite
with min(Tc) for ModuleX with min(defect leakage
rate) within min(testing time frame)?
8. Role of Predictive Analytics In QA
● TC Prioritization in RR
● Resource utilization
● Report generation
10. TCP ?
● Focus on ranking all existing TC without eliminating.
Detect Fault Soon.
● Executes TC's in given order until the testing budget is
exhausted.
12. How TCP ?
Techniques for TCP
● Text diversity-based Prioritization
AllDist(Ti,PS,d)= Min{d(Ti,Tj)} | Tj PS
● Topic diversity-based
● History Based clustering
● C 1 = { tc x — tc x 2 FT(n) }
● C 2 = { tc x — tc x 62 C1 AND tc x 2 FT(n-1) }
● C 3 = { tc x — tc x 62 [(C 1 ,C 2 ) AND tc x 2 FT(n-2)}
13. Inputs For TCP.
● Change information
● Historical Fault detection
● Dynamic and Static Coverage Data
● SRD
● Test Scripts
15. System Under Test
Type Release Total Test New Test %New Test Median Old
Test
TR 3.0 580 398 68% 1
RR 5.5 1055 39 4% 4
Type Release Release
Date
No. Of test No. of Faults Failure Rate
RR 3.0 1/12/2016 580 127 21.90%
RR 4.0 25/12/2016 1055 6 0.57%
16. K Mean Clustering
● Assume Euclidean space/distance
● Start picking k , the number of clusters
● Initialize clusters by picking one point per clusters and
find the minimum distance
● Repeat for all the clusters
18. ● Resource allocation predictions based on the Analysis
● Predict the success rate of project with n number of
resources having 5+ years of domain expertise QA
within min(time_frame)
Resource Allocation Problems
19. Understand your Resource
● Identifying the Performance [Demographic, Gender
Biased, Skills]
● Resource Allocation in RR & TR
● Resources Churn Detection
20. Data Sets
Proje
ct
ID Age Gend
er
Marital
Status
Issues
Reporte
d
Priori
ty of
Bug
Relea
se
Time
Locat
ion
Project
Complexit
y
aaa a123 23 M S 12 xx xx xx xx
Project Complexity Age Gender Domain Expertise Interest Level
xx xx xx xx xx xx xx
22. Reports
Metrics That Matters
● Analytical Reports
– Add values to current test tools generated reports
on better explaining the data collected and will be
useful for future prediction and forecasting.
23. Metrics That Matters
● Measuring the Doneness
● Resource Allocation
● Measuring Performance and biases
● Beyond the Check Marks
