Transcript
Page 1: Software Defect Prediction & Release Readiness Assessment

Software Defect Prediction & Release Readiness Assessment

Rakesh Rana

Page 2: Software Defect Prediction & Release Readiness Assessment

Rakesh Rana

PhD in Software Engineering, 2015

Double Masters from Handelshögskolan i Göteborg

Tekn. Lic in 2013

Industrial collaborations with Volvo Car Group, Ericsson, Saab

> 15 publications

About Myself

Page 3: Software Defect Prediction & Release Readiness Assessment

Number of defects / trouble reports (TRs)

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Number of defects / trouble reports (TRs)

Have you ever wondered?

• What is the trend of defect inflow, what does it indicate?

• Have we found most defects already or will we find them late?

• How many defects are we likely to find in this project/release?

• Are we ready to release? or do we need to test more?

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Data used

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Early or late defects?

ConcaveS-shape Convex

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Early or late defects?

Projects/

Releases

Defect inflow intensity trend until half-way through the project Predicted

shape of

defect inflow

profile

Overall

trend

Trend after

reaching

maximum

Defect inflow intensity trend characteristics

A1, A3, A4

& C1Increasing Decreasing

Defect inflow intensity first increases, maximizes near

to half-way and then decreasesS-shape

B1, B3 &

B4Decreasing Decreasing

Early defects, defect inflow intensity maximum early

then decreases smoothlyConcave

A2, B2, B5

& C2Increasing Increasing

Late defects, defect inflow intensity trend is positive

throughout half-way of project timelineConvex

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Using predicted shape to choose a better model

Predicted

shape of

defect inflow

profile

Recommended SRGMs

For testing resource(s)

allocation

For release readiness

assessment using

current project data

S-shape Logistic Logistic

Concave Gompertz Gompertz

Convex Delayed-S Logistic

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Predicting Defect Inflow (Shape & Expected No)

is useful for:

– Software engineers to make early intervention in design and

implementation to take corrective actions early,

– Quality assurance managers to plan and allocate human and

test resources optimally, and

– Project managers to manage release cycle decisions and

monitor progress.

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Defect Predictions: Other useful techniques

• Predicting which files/modules are most likely to have a defect:

– Regression Models

– Machine Learning Models

File/module likely tocontain defect/not

No ofChanges

Size

Complexity

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Defect Prediction: Assessing Release Readiness

Proj/Release-1LOC: 100KDefects found: 524Defect density: 5.24/KLOC

Proj/Release-4LOC: 250KDefects found: 1063Defect density:

Proj/Release-2LOC: 162KDefects found: 992Defect density: 6.12/KLOC

Proj/Release-3LOC: 550KDefects found: 3196Defect density: 5.81/KLOC

Historical Data Current Project

4.5/KLOC

Comparing defect density

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Defect Prediction: Assessing Release Readiness

Defect seeding

Automated

Test

Manual

Test

Remaining defectdensity is proportional to not found seededdefects densityDevelopment

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Defect Prediction: Assessing Release Readiness

Defect pooling

Test Suite-A

Team-A

Test Suite-B

Team-B

or

Uniquedefects

(A)

Uniquedefects

(B)

Common defects (A,B)

More common defects -> less defects remaining in the system

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• For more information on:

– Analysing defect inflow data,

– Release readiness assessment, or

– Using metrics for process improvement.

• Contact:

– Research theme: Metrics

• Miroslaw Staron, [email protected]

• Rakesh Rana, [email protected]


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