The document discusses detecting and removing bad code smells from source code. It describes a tool called HIST that uses change history information to detect code smells. The tool extracts change history from version control logs, analyzes the code changes, and uses the information to detect five types of code smells: divergent change, shotgun surgery, parallel inheritance, blob, and feature envy. An evaluation of HIST shows it improves on static code analysis techniques in detecting these code smells, with F-measure scores for detection ranging from 64-92% compared to 0-48% for static analysis alone. The document concludes by discussing some of the researcher's ongoing and future work related to code smells and refactoring.

1. Detecting and Removing Bad Code
Smells from Source Code
A review of my first PhD year
Fabio Palomba
PhD Symposium 2014

6. Technical Debt is...
“Not quite right code which we
postpone making it right.”
W. Cunningham

7. Technical Debt is...
“Not quite right code which we
postpone making it right.”
W. Cunningham
Low Internal Quality
Documentation Debt
Coding Style Violation
Code Smell
Test debt
Architectural Debt Structural Debt
Code Complexity

8. Bad Code Smells

10. [Abbes et al. CSMR 2011]

11. [Abbes et al. CSMR 2011]
Bad Smells hinder code
comprehensibility

12. [Khomh et al. EMSE 2012]

13. [Khomh et al. EMSE 2012]
Bad Smells increase change
and fault proneness

14. [Banker et al. Comm. of the ACM 1993]

15. [Banker et al. Comm. of the ACM 1993]
Bad smells increase
maintenance costs

16. F. Palomba, A. De Lucia, G. Bavota, R. Oliveto.
“Anti-Pattern Detection: Methods, Challanges, and Open Issues”
Advances in Computers, vol. 95 (to appear)

17. Many detection techniques
use structural analysis

18. Many bad smells are intrinsically characterized
by how code elements change over time, rather
than by structural properties

19. HIST
Historical Information
for Smell deTection

20. HIST
Historical Information
for Smell deTection

21. HISTChange History Extractor
Historical Information
for Smell deTection

22. HISTChange History Extractor
log download
Historical Information
for Smell deTection

23. HISTChange History Extractor
log download code analyzer
Historical Information
for Smell deTection

24. HISTChange History Extractor
log download code analyzer
Historical Information
for Smell deTection
method
getUser
has been
added

25. HISTChange History Extractor
log download code analyzer
Historical Information
for Smell deTection
method
getUser
has been
added
method
getData
has been
modified

26. HISTChange History Extractor
log download code analyzer
Historical Information
for Smell deTection
method
getUser
has been
added
method
getData
has been
modified
method
getLogin
has been
moved

27. HISTChange History Extractor
log download code analyzer
Historical Information
for Smell deTection
method
getUser
has been
added
method
getData
has been
modified
method
getLogin
has been
moved
class
User
has been
added

28. HISTChange History Extractor
log download code analyzer
Historical Information
for Smell deTection
method
getUser
has been
added
method
getData
has been
modified
method
getLogin
has been
moved
class
User
has been
added
...
...
...
...

29. HISTChange History Extractor
log download code analyzer
Code Smells Detector
Historical Information
for Smell deTection
method
getUser
has been
added
method
getData
has been
modified
method
getLogin
has been
moved
class
User
has been
added
...
...
...
...

30. HISTChange History Extractor
log download code analyzer
Code Smells Detector
Historical Information
for Smell deTection
method
getUser
has been
added
method
getData
has been
modified
method
getLogin
has been
moved
class
User
has been
added
...
...
...
...
Association rule
discovery to capture co-changes
between entities

31. HISTChange History Extractor
log download code analyzer
Code Smells Detector
Historical Information
for Smell deTection
method
getUser
has been
added
method
getData
has been
modified
method
getLogin
has been
moved
class
User
has been
added
...
...
...
...
Association rule
discovery to capture co-changes
between entities
Analysis of change
frequency of some
specific entities

32. HISTCode Smell Detector
Five different smells considered
Divergent Change
Shotugun Surgery
Parallel Inheritance
Historical Information
for Smell deTection

33. HISTCode Smell Detector
Five different smells considered
Three code smells, by definition,
to consider as “historical” smells
Divergent Change
Shotugun Surgery
Parallel Inheritance
Historical Information
for Smell deTection

34. HISTCode Smell Detector
Five different smells considered
Three code smells, by definition,
to consider as “historical” smells
Divergent Change
Shotugun Surgery
Parallel Inheritance
Blob
Feature Envy
Historical Information
for Smell deTection

35. HISTCode Smell Detector
Five different smells considered
Three code smells, by definition,
to consider as “historical” smells
Divergent Change
Shotugun Surgery
Parallel Inheritance
Two code smells generally
detected by structural analysis,
where the historical analysis can
aid in the identification of
complementary information
Blob
Feature Envy
Historical Information
for Smell deTection

36. Code Smells Detector
divergent change

37. Code Smells Detector
divergent change
A class is changed in different
ways for different reasons

38. Code Smells Detector
divergent change
A class is changed in different
ways for different reasons
Solution:
Extract Class Refactoring

39. Code Smells Detector
divergent change
A class is changed in different
ways for different reasons
Solution:
Extract Class Refactoring
Detection
Classes containing at least two sets of methods
such that:
(i) all methods in the set change together as
detected by the association rules
(ii) each method in the set does not change with
methods in other sets

40. Code Smells Detector
blob

41. Code Smells Detector
blob
A class implementing several
responsibilities, having a large size,
and dependencies with data classes

42. Code Smells Detector
blob
A class implementing several
responsibilities, having a large size,
and dependencies with data classes
Solution:
Extract Class refactoring

43. Code Smells Detector
blob
A class implementing several
responsibilities, having a large size,
and dependencies with data classes
Solution:
Extract Class refactoring
Detection
Blobs are identified as classes frequently modified in
commits involving at least another class.
t1 t2 t3 t4 t5 t6 t7 t8 t9

44. Evaluation
detection accuracy

45. Evaluation
detection accuracy
20 open source
systems

46. Evaluation
detection accuracy
Comparing HIST with static code
analysis technique on a manually
built oracle
20 open source
systems

47. Evaluation
detection accuracy
20 open source
systems
Shotgun Surgery
Parallel Inheritance
Divergent Change
Blob
Feature Envy
Comparing HIST with static code
analysis technique on a manually
built oracle
HIST F-Measure CA technique F-Measure
92%
71%
82%
64%
77%
0%
9%
11%
48%
68%

48. Evaluation
detection accuracy
Comparing HIST with static code
analysis technique on a manually
built oracle
HIST and
the CA techniques
20 open source
systems
are highly complementary

49. 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” - Distinguished Paper Award
International Conference on Automated Software Engineering (ASE 2013)

50. 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” - Distinguished Paper Award
International Conference on Automated Software Engineering (ASE 2013)

51. F. Palomba, G. Bavota, M. Di Penta, R. Oliveto, D. Poshyvanyk and A. De Lucia.
“Mining Version Histories for Detecting Code Smells”
Transactions on Software Engineering (TSE) - 2015

52. http://www.sesa.unisa.it/landfill
F. Palomba, D. Di Nucci, M. Tufano, G. Bavota, M. Di Penta, R. Oliveto, D. Poshyvanyk and A. De Lucia.
“Landfill: an Open Dataset of Code Smells with Public Evaluation”
Submitted to the International Conference on Software Engineering (ICSE 2015)

53. HIST
Mar
16 The tool is coming soon...
Historical Information
for Smell deTection

54. Do They Really Smell Bad?
A Study on Developers’ Perception of
Bad Code Smells
Fabio Palomba, Gabriele Bavota, Massimiliano Di Penta,
Rocco Oliveto, Andrea De Lucia
ICSME 2014
Victoria - Canada
October 1st, 2014

55. “We don’t see things as they are,
we see things as we are”
Anais Nin

56. Investigating the
developers’ point of view

57. RQ1: To what extent do bad smells reflect developers‘
perception of design problems?
Investigating the
developers’ point of view

58. RQ1: To what extent do bad smells reflect developers‘
perception of design problems?
RQ2: What are the bad smells that developers feel
as the most harmful?
Investigating the
developers’ point of view

59. Smells generally not Perceived as
Design or Implementation Problems
THEORical
problems

60. Smells generally Perceived and
Identified by Respondents
THEY SHOULD BE
CAREFULLY ANALYZED

61. Smells whose Perception may Var y
dependently by Their Intensit y
THEY SHOULD BE
studied more

62. On the Innate Relationship between
Quality and Refactoring

63. Refactoring is...
“Refactoring is a disciplined technique for restructuring an
existing body of code, improving its non-functional
attributes without changing its external behavior.”

64. 3 open source
systems
Studying if quality metrics
/ bad smells capture
refactoring operations

65. 3 open source
systems
11 quality metrics
Studying if quality metrics
/ bad smells capture
refactoring operations

66. 3 open source
systems
11 quality metrics
11 bad smells
Studying if quality metrics
/ bad smells capture
refactoring operations

67. 3 open source
systems
11 quality metrics
11 bad smells
12,922 refactoring operations
manually validated
Studying if quality metrics
/ bad smells capture
refactoring operations

68. G. Bavota, A. De Lucia, M. Di Penta, R. Oliveto and F. Palomba.
“An Experimental Investigation on the Innate Relationship between Quality and Refactoring”
Submitted to Transactions on Software Engineering and Methodology (TOSEM)

69. http://www.sesa.unisa.it/tools/aries.jsp
F. Palomba, M. Tufano, G. Bavota, R. Oliveto, A. Marcus, D. Poshyvanyk, and A. De Lucia.
“Extract Package Refactoring in ARIES”
Submitted to the International Conference on Software Engineering (ICSE 2015)

70. Moving Forward

71. M. Tufano, F. Palomba, G. Bavota, R. Oliveto, M. Di Penta, A. De Lucia, and D. Poshyvanyk
“When and Why Your Code Starts to Smell Bad”
Submitted to the International Conference on Software Engineering (ICSE 2015)

72. F. Palomba
“Textual Analysis for Code Smell Detection”
Submitted to the International Conference on Software Engineering (ICSE 2015)

73. Learning from the past for
prioritizing bad code smells

74. Technical Debt is...
“Not quite right code which we
postpone making it right.”
W. Cunningham
Low Internal Quality
Documentation Debt
Coding Style Violation
Code Smell
Test debt
Architectural Debt Structural Debt
Code Complexity

75. Fabio Palomba
fpalomba.unisa.it
dibt.unimol.it/fpalomba
Research is
a work of