Combining techniques for software quality classification: An integrated decision network

approach

Ruben de Jong

Author

• Name: Nan-Hsing Chiu.• Ching Yun University (Zhongli City, Taiwan)

– Assistant professor.– Chairman of Department of Information

Management.• Specializes in:

– Artificial Intelligence.– Software Engineering.– Engineering.

Software module quality

• Determine likelihood whether a software module contains faults.

• Software quality classification models– Takes feature values.– Output fault proneness (fp) or not fault proneness

(nfp).• How to combine these models?

– Some models perform better on certain modules.– Simply take the average result?

Integrated decision networkFeature value 1

Feature value 2

Feature value 3

CART

C4.5

CARTresult

CART inverted

result

C4.5 inverted

result

C4.5 result

Output: nfp

Weight 0.85

Weight 0.10

Weight 0.70

Weight 0.15

fp

nfp

1

0

0

1

threshold = 1.2

• Feature values are used input for software quality classification models.– Models each get a result (fp or nfp).

Integrated decision networkFeature value 1

Feature value 2

Feature value 3

CART

C4.5

CARTresult

CART inverted

result

C4.5 inverted

result

C4.5 result

Output: nfp

Weight 0.85

Weight 0.10

Weight 0.70

Weight 0.15

fp

nfp

1

0

0

1

threshold = 1.2

• Particle swarm optimization is applied to search the optimal combination of results.– If the summed end value exceeds a certain threshold, the

module is fp, otherwise it is nfp.

Acquire feature values

Combine classification models

Acquire software module feature values

Generate result nodes

Determine weights

Calculate software module result

[else]

[sufficient fitness]

Select software module

SOFTWARE MODULES

FEATURE VALUE

1

1..*

1

1..*

1

MODEL BASE1..* 1

d

RESULT NODE

valueweight

INVERSE RESULT NODE

valueweight

NORMAL RESULT NODE

valueweight

1

1

1

1..*

1

is derived from

outputs

properties to be tested

Quality Assurance

Computer

Computer

ComputerCALCULATED

SOFTWARE MODULE

Software quality class

UNCALCULATED SOFTWARE MODULE

Calculate classification model results

SOFTWARE QUALITY CLASSIFICATION MODEL

Classified result

1..*

Related literature

• Software quality classification models:– Classification and Regression Trees (CART) model

(Khoshgoftaar, Allen, Jones, & Hudepohl, 2000).– SPRINT model (Khoshgoftaar & Seliya, 2002).– Artificial Neural Networks (ANN) model (Thwin &

Quah, 2005).• Basis for integrated decision network.

• Multiple models are better than a single model (Jeffery et al., 2001).

Related literature

• Relying on a single model poses a risk (Macdonell & Shepperd, 2003).

• Early attempts at combining had a limited scope (Schapire, 1999).

• Combination through decision tree classifiers performed better than alternatives (Bouktif, Sahraoui, & Kégl, 2002).

Example

Feature value 1

Feature value 2

Feature value 3

CART

C4.5

CARTresult

CART inverted

result

C4.5 inverted

result

C4.5 result

Output: nfp

Weight 0.85

Weight 0.10

Weight 0.70

Weight 0.15

fp

nfp

1

0

0

1

threshold = 1.2

Questions?