WCRE12d.ppt

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The document discusses a study examining whether lexicon bad smells (LBS) can improve fault prediction. It finds that LBS provide new information compared to structural metrics. Models using both LBS and structural metrics showed improved fault prediction over structural metrics alone, with an average prediction improvement of 29-36%. The most harmful LBS across systems were inconsistent terms, overloaded identifiers, and synonyms with similar names. System-specific most harmful LBS included extreme contractions and misspellings. The document concludes that LBS are easy to understand, avoid, and fix.

Technology Education

Can Lexicon Bad Smells
improve fault prediction?
Surafel Abebe, Venera Arnaoudova, Paolo Tonella,
Giuliano Antoniol and Yann-Gaël Guéhéneuc

1 WCRE’12

Fault prediction

Motivation - take advantage from past
mistakes

Scenario

set of metrics

prediction technique and conﬁguration

evaluation metrics

Venera Arnaoudova 2 WCRE’12

Set of metrics

Structural - CK suit

Coupling between objects (CBO)

Depth of Inheritance Tree (DIT)

...

Linguistic - Lexicon Bad Smells (LBS)

Venera Arnaoudova 3 WCRE’12

Lexicon Bad Smells (LBS)
Terms Structural Meaning Naming Rules
aSz addition () foo() nameString
Extreme Odd grammatical Meaningless terms Useless type
contraction structure indication

Similar names Identiﬁer construction
Misspelled Overloaded
terms rules
identiﬁers
Wrong context

whole and parts

Wiki:
Conceptual Hierarchy
http://selab.fbk.eu/LexiconBadSmellWiki

No hyponyms

Inconsistent term

Venera Arnaoudova 4 WCRE’12

Technique
Principal Component Analysis (PCA)

Prediction models

Logistic Regression (LR)

Random Forest (RF)

Support Vector Machine (SVM)

within and next version

Venera Arnaoudova 5 WCRE’12

Evaluation metrics
Rank

popt, Fault Percentile Average (FPA)

Classiﬁcation

Accuracy, F-measure, MCC

Error

Absolute error

Venera Arnaoudova 6 WCRE’12

Research questions

RQ1: Do LBS bring new information with
respect to structural metrics?

RQ2: Do LBS improve fault prediction?

RQ3: Which LBS harm more?

Venera Arnaoudova 7 WCRE’12

Case Study

System # versions # classes

ArgoUML 6 > 800 - 1400 <

Eclipse 5 > 4500 - 6800 <

Rhino 8 > 90 - 190 <

Venera Arnaoudova 8 WCRE’12

RQ1: LBS bring new
information?

PCA

Component selection: 95% variance

Attribute selection: 10% relative threshold

Venera Arnaoudova 9 WCRE’12

RQ1: LBS bring new
information?

Inconsistent terms was retained in all
systems

90% of the time inconsistent terms and
overloaded identiﬁers were the main
contributor in at least one of the retained
PC

Venera Arnaoudova 10 WCRE’12

RQ2: LBS improve fault
prediction?
Select one model SVM
Build prediction models using the CK suit

Rank models using the evaluation metrics

CK+LBS vs CK

net improvement within/across versions

average delta percentage

Venera Arnaoudova 11 WCRE’12

RQ2: LBS improve fault
prediction?
Average delta percentage: 29/36 predictions

Net improvement:
Within version Next version
4-5/6 predictions 3-4/5 predictions
ArgoUML (all metrics) (half of the metrics)
5/5 predictions 1-2/4 predictions
Eclipse (all metrics) (half of the metrics)

4-7/8 predictions 1-4/5 predictions
Rhino (all metrics) (all metrics)

Venera Arnaoudova 12 WCRE’12

RQ3: Which LBS harm
more?

Rank LBS according to their weight assigned
by the model

Median over the different models

Venera Arnaoudova 13 WCRE’12

RQ3: Which LBS harm
more?

Common to all systems:

synonym similar getObject() and acquireObject()

inconsistent terms INITIAL_WIDTH and WIDTH

overloaded identiﬁers matchOrReplace()

Venera Arnaoudova 14 WCRE’12

RQ3: Which LBS harm
more?

System speciﬁc

extreme contraction (Eclipse)

misspelling (Rhino)

Venera Arnaoudova 15 WCRE’12

So what?

Lexicon Bad Smells are easy to ...

understand

avoid

ﬁx

Venera Arnaoudova 16 WCRE’12

Conclusion

Venera Arnaoudova 17 WCRE’12

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