Ehsan Salamati Taba, Foutse Khomh,
Ying Zou, Meiyappan Nagappan,
Ahmed E. Hassan
1

2

Past Defects, History of Churn (Zimmermann, Hassan et al.)
Model
Predict Bugs
Topic Modeling (Chen et al.)
3

4

 weaknesses in design
 not technically incorrect and don't prevent a system from functioning
5

6

Antipatterns indicate weaknesses in the design that may increase the risk for bugs in the future.
(Fowler 1999)
7

CVS Repository
Bugzilla
Mining Source Code
Repositories
Detecting
Antipatterns
Mining Bug
Repositories
Calculating Metrics Analyzing
RQ1
RQ2
RQ3
9

Systems
Systems
Eclipse
Release(#)
2.0 - 3.3.1(12)
ArgoUML 0.12 - 0.26.2(9)
Churn LOCs
148,454 26,209,669
21,427 2,025,730
10

 DECOR (Moha et al.)
 13 different antipatterns
Systems #Antipatterns
Eclipse 273,766
ArgoUML 15,100
# of Antipatterns
# Files
12

RQ1: Do antipatterns affect the density of bugs in files?
RQ2: Do the proposed antipattern based metrics provide additional explanatory power over traditional metrics?
RQ3: Can we improve traditional bug prediction models with antipatterns information?
14

 Density of bugs in the files with antipatterns and the other files without antipatterns is the same.
15

Systems Releases(#) D
A
– D
NA
> 0 p-value<0.05
Eclipse 12 8 8
ArgoUML 9 6 6
Files with
Antipatterns
Density of Bugs
Files without
Antipatterns
Density of Bugs
16

RQ1: Do antipatterns affect the density of bugs in files?
RQ2: Do the proposed antipattern based metrics provide additional explanatory power over traditional metrics?
RQ3: Can we improve traditional bug prediction models with antipatterns information?
17

 Average Number of Antipatterns (ANA)
 Antipattern Recurrence Length(ARL)
 Antipattern Cumulative Pairwise Differences
(ACPD)
 Antipattern Complexity Metric (ACM)
18

a.java
b.java
1.0
2.0
3.0
4.0
5.0
6.0
3 4 0 2 1 3
4 5 1 0 0 3 c.java
0 6 5 4 5 4
ANA(a.java) =2.16, ARL(a.java) = 18.76,
ACPD(a.java) = 0
19

20

 Provide additional explanatory power over traditional metrics
 ARL shows the biggest improvement
21

RQ1: Do antipatterns affect the density of bugs in files?
RQ2: Do the proposed antipattern based metrics provide additional explanatory power over traditional metrics?
RQ3: Can we improve traditional bug prediction models with antipatterns information?
22

Metric name
LOC
MLOC
PAR
NOF
NOM
NOC
VG
DIT
LCOM
NOT
WMC
PRE
Churn
Description
Source lines of codes
Executable lines of codes
Number of parameters
Number of attributes
Number of methods
Number of children
Cyclomatic complexity
Depth of inheritance tree
Lack of cohesion of methods
Number of classes
Number of weighted methods per class
Number of pre-released bugs
Number of lines of code added modified or deleted
Step-wise analysis
1) Removing Independent
Variables
2) Collinearity Analysis
23

Eclipse
12
10
8
6
4
2
0
 ARL remained statistically significant and
8
6
4
2
0
Churn PRE LOC MLOC NOT NOF NOM ACM ACPD ARL
24

 ARL can improve crosssystem bug prediction on the two studied systems
25

27

a.java
b.java
1.0
2.0
3.0
4.0
5.0
6.0
3 4 0 2 1 3
4 5 1 0 0 3 c.java
0 6 5 4 5 4
ANA(a.java) =2.16, ARL(a.java) = 18.76,
ACPD(a.java) = 0
28

Anti Singleton Blob Class Data Should be Private
Lazy Class LPL Long Method
Spaghetti Code SG SwissArmyKnife
Complex Class Large Class
Message Chain RPB
-
29

30

There is no difference between the density of future bugs of the files with antipatterns and the other files without antipatterns.
Findings
In general, the density of bugs in a file with antipatterns is higher than the density of bugs in a file without antipatterns.
31