Code smells represent symptoms of poor implementation choices. Previous studies found that these smells make source code more difficult to maintain, possibly also increasing its fault-proneness. There are several approaches that identify smells based on code analysis techniques. However, we observe that many code smells are intrinsically characterized by how code elements change over time. Thus, relying solely on structural information may not be sufficient to detect all the smells accurately. We propose an approach to detect five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy, by exploiting change history information mined from versioning systems. We applied approach, coined as HIST (Historical Information for Smell deTection), to eight software projects written in Java, and wherever possible compared with existing state-of-the-art smell detectors based on source code analysis. The results indicate that HIST’s precision ranges between 61% and 80%, and its recall ranges between 61% and 100%. More importantly, the results confirm that HIST is able to identify code smells that cannot be identified through approaches solely based on code analysis.

1. Fabio Palomba
DETECTING BAD SMELLS IN SOURCE CODE
USING CHANGE HISTORY INFORMATION
Universityofsalerno
fisciano,27/09/2013
Candidate:
Fabio Palomba
@fabiopalomba3
ADVISORS:
Andrea De Lucia
Gabriele Bavota
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2. TODAY, I’LL SPEAK ABOUT...
Bad Code: causes and effects
Software Evolution and Code Quality
HIST: Historical Information for
Smell deTection
A Method to Detect Bad Smells using
Historical Informations
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3. SOFTWARE EVOLUTION
AND CODE QUALITY
PART I
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4. Cohesion
Coupling
W. Stevens, G. Myers, and L. Constantine. Structured design. IBM Systems Journal,
13(2):115 - 139, 1974.
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5. sabato 28 settembre 13

6. Victor R. Basili, Lionel C. Briand, and Walcélio L. Melo.A validation of object-
oriented design metrics as quality indicators. IEEETransactions on Software
Engineering, 22(10):751 - 761, 1996.
L. C. Briand, J.Wüst, J.W. Daly, andV. D. Porter. Exploring the relationship between
design measures and software quality in object- oriented systems. Journal of
Systems and Software (JSS), 51(3):245 - 273, 2000.
DEFECTS
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7. COMPREHENSIBILITY
M.Abbes, F. Khomh,Y.-G. Guéhéneuc, and G.Antoniol,“An empirical study of the impact
of two antipatterns, blob and spaghetti code, on program comprehension,” in 15th
European Conference on Software Maintenance and Reengineering, CSMR 2011.
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8. Tibor Gyimóthy, Rudolf Ferenc, and István Siket. Empirical validation of object-
oriented metrics on open source software for fault prediction. IEEETransactions
on Software Engineering (TSE), 31(10):897 - 910, 2005.
PRODUCTIVITY
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9. 2 - 100
MAINTENANCE COSTS ARE
GREATER THAN DEVELOPMENT COSTS
R.D. Banker, S. M. Datar, C. F. Kemerer, D. Zweig. Software complexity and
maintenance costs. Communications of the ACM, v.36 n.11, pages 81 - 94, 1993.
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10. HIST:
HISTORICAL
INFORMATION
FOR SMELL
DETECTION
PART II
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11. An AntiPattern is a literary form that describes a
commonly occurring solution to a problem that
generates decidedly negative consequences
William H. Brown, Raphael C. Malveau, Hays W. McCormick,Thomas J. Mowbray -
Antipatterns: Refactoring Software,Architectures and Project in Crisis
22 14
BAD SMELLS: DEFINITION
DEVELOPMENT ANTIPATTERNSsabato 28 settembre 13

12. DETECTION VIA STRUCTURAL ANALYSIS
“Code smell are structural characteristics of software
that may indicate a code or design problem.”
F. Fontana et al. - “Automatic detection of bad smells in code:An experimental assessment”, Journal of ObjectTechnology
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13. DETECTION VIA STRUCTURAL ANALYSIS
“Code smell are structural characteristics of software
that may indicate a code or design problem.”
Many bad smells are intrinsically characterized
by how code elements change over time,
rather than by structural properties!
F. Fontana et al. - “Automatic detection of bad smells in code:An experimental assessment”, Journal of ObjectTechnology
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14. BLOB Feature
envy
Divergent
change
shotgun
surgery
Parallelinheritance
hierarchies
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15. HIST PROCESS
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16. HIST PROCESS
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17. EXTRACTING CHANGE HISTORY
Changes at method-level are captured
using a code-analyzer developed in
the Markos European Project*
* http://markosproject.berlios.de
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18. HIST PROCESS
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19. HIST PROCESS
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20. DETECTION ALGORITHMS
Divergent change occurs when one class is commonly
changed in different ways for different reasons.
Classes having at least two sets of methods
changing together but independently
from methods in the other sets
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
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21. DETECTION ALGORITHMS
Divergent change occurs when one class is commonly
changed in different ways for different reasons.
Classes having at least two sets of methods
changing together but independently
from methods in the other sets
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
sabato 28 settembre 13

22. DETECTION ALGORITHMS
Divergent change occurs when one class is commonly
changed in different ways for different reasons.
Classes having at least two sets of methods
changing together but independently
from methods in the other sets
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
sabato 28 settembre 13

23. MINING VERSION HISTORIES THROUGH
ASSOCIATION RULE DISCOVERY
C
E
D
B
A
Files
Changesoccurringinsnapshots
S1 S2 S3 S4 S5 S6 S7 S8
Thomas Zimmermann, Peter Weißgerber, Stephan Diehl,Andreas Zeller: MiningVersion Histories to Guide
Software Changes. ICSE 2004: 563-572
AnnieT.T.Ying, Gail C. Murphy, RaymondT. Ng, Mark Chu-Carroll: Predicting Source Code Changes by Mining
Change History. IEEETrans. Software Eng. 30(9): 574-586 (2004)
A
C
B
D
A
D
B
D
E
A
C
B
D
E
A
C
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24. MINING VERSION HISTORIES THROUGH
ASSOCIATION RULE DISCOVERY
C
E
D
B
A
Files
Changesoccurringinsnapshots
S1 S2 S3 S4 S5 S6 S7 S8
Thomas Zimmermann, Peter Weißgerber, Stephan Diehl,Andreas Zeller: MiningVersion Histories to Guide
Software Changes. ICSE 2004: 563-572
AnnieT.T.Ying, Gail C. Murphy, RaymondT. Ng, Mark Chu-Carroll: Predicting Source Code Changes by Mining
Change History. IEEETrans. Software Eng. 30(9): 574-586 (2004)
A
C
B
D
A
D
B
D
E
A
C
B
D
E
A
C
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25. Classes containing at least one method
changing together with methods
contained in more than δ(δ=3) different classes
DETECTION ALGORITHMS
You have a Shotgun Surgery when every time you make
a kind of change, you have to make a lot of little changes to
a lot of different classes.
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
sabato 28 settembre 13

26. Pairs of classes for which the addition
of a subclass implies the addition of a subclass
for the other class
You have a Parallel Inheritance Hierarchies when
every time you make a subclass of one class, you also have to
make a subclass of another.
DETECTION ALGORITHMS
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
sabato 28 settembre 13

27. DETECTION ALGORITHMS
A Blob is a class implementing several responsibilities, having
a large number of attributes, operations and dependencies
with data classes.
Classes modified (in any way) in more than α%
(α =8) commits involving at least another class
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
sabato 28 settembre 13

28. DETECTION ALGORITHMS
A Feature Envy occurs when a method is more interested
in a class other than the one it is actually in.
Methods involved in commits with methods of
another class of the system β% (β =70) more
than commits with methods of their class
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
sabato 28 settembre 13

29. EMPIRICAL EVALUATION
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30. CASE STUDY DESIGN
RQ1:Which are the performance of
HIST in detecting bad smells?
RQ2: How does HIST compares to the
techniques based on structural analysis?
RQ System Metrics
ApacheTomcat
Apache Ant
JEdit
5 API Android
ApacheTomcat
Apache Ant
JEdit
5 API Android
Precision
Recall
F-Measure
Precision
Recall
F-Measure
Correctti ∩ Correcttj
Correctti Correcttj
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
sabato 28 settembre 13

31. 10 % (20 - 7)
0 % (0 - 0)
7 % (4 - 45)
50 % (52 - 49)
63 % (68 - 60)
76 % (73 - 79)
89 % (80 - 100)
61 % (61 - 61)
68 % (76 - 61)
76 % (71 - 81)
RESULTS
Bad smell HIST Code analysis technique
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
Divergent Change
Shotgun Surgery
Parallel Inheritance
Blob
Feature Envy
F-Measure (Precision - Recall) F-Measure (Precision - Recall)
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32. 48 %
41 %
17 %
39 %
35 %
20 %
40 %43 % 17 %
RESULTS
F. Palomba, G. Bavota, M. Di Penta, R. Oliveto,A. De Lucia and D. Poshyvanyk,“Detecting Bad Smells in Source
Code Using Change History Information”, in the 28th IEEE/ACM International Conference
on Automated Software Engineering (ASE’13), 2013
0 % 93 %
100 %0 %
7 %
0 %
Bad smell HIST ∩ CA HIST CA CA HIST
Divergent Change
Shotgun Surgery
Parallel Inheritance
Blob
Feature Envy
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33. Conclusions & Future work
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34. CONCLUSION
“This paper seems to bring some fresh air
into an area that has not seen
breakthroughs for some time.”
[One of the ASE Reviewers]
+ PRECISION
+ F - MEASURE
+ RECALL
+ Historical analysis for smell
detection
- Historical information is
needed
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35. CAN WE DEFINE AN HYBRID
APPROACH TO DETECT BAD SMELLS?
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36. CAN WE USE HIST TO DETECT
OTHER BAD SMELLS?
Spaghetti Code
Long Method
Complex Class
Refused Bequest
Duplicate Code
Primitive Obsession
Middle Man
Data Clump
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37. Fabio Palomba
fabiopalomba13
fabio.palomba.89@gmail.com
http://www.linkedin.com/pub/fabio-palomba/4a/542/60
University of Salerno
DETECTING BAD SMELLS IN SOURCE CODE
USING CHANGE HISTORY INFORMATION
Thank you!
Questions and/or comments
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