SAIL, School of Computing, Queen’s University, Kingston, Canada

A Case Study of Bias in Bug-Fix Datasets

Thanh H. D. Nguyen, Bram Adams, Ahmed E. Hassan

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We need bug prediction• Problem:

• Quality improvement resource is limited.• Solution:

• Bug prediction identifies defect-prone modules.

Our focus is data quality

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What if there is sample

bias?

We should consider bias in our studies

Stanford graduate student housing survey

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#1

#2

#2

Unlinked bugs have:Higher severityLess experience[Bird al et. 2009] Linkage Bias

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#1

#2

#2

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#1

#2

#2

Tagging BiasAbout 2/3 of all bugs

reports are not defects[Antoniol al et. 2008].

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Biases are threats to validity of software quality studies

• Because of linkage bias, our models:• neglect higher severity bugs.• neglect less experienced developers.

• Because of tagging bias, our models:• inaccurately consider more bugs that existed.

Do biases really exist? How do biases

affect our research?

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Near ideal data:Linkage is enforced.Tagging is provided.

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Severity

Experience

Maturity

Release pressure

Collaboration

✔✔−−−

−✔✔−−

Conjecture: Biases are properties of the

software process, not of missing links.

Do linkage biases exist in Jazz?

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Severity

Experience

Maturity

Release pressure

Collaboration

✔✔−−

✔

Question:How does

tagging biases affect our research?

Do tagging biases exist in Jazz?

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How tagging biases affect our research?

Files Defects + Tasks

A 5B 4C 6D 1

Defects only

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Not biasWhich we should use

BiasWhich we

normally use

Spearman: .94Pearson: .97

Conjecture: It might be ok to

use biased data.

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