Detecting Bad Smells in Source Code using Change History Information

Fabio Palomba

DETECTING BAD SMELLS IN SOURCE CODE USING CHANGE HISTORY INFORMATION

University of salernofisciano, 27/09/2013

Candidate:

Fabio Palomba@fabiopalomba3

ADVISORS:

Andrea De Lucia Gabriele Bavota

sabato 28 settembre 13

TODAY, I’LL SPEAK ABOUT...

Bad Code: causes and effectsSoftware Evolution and Code Quality

HIST: Historical Information for Smell deTectionA Method to Detect Bad Smells using Historical Informations


SOFTWARE EVOLUTION AND CODE QUALITY

PART I


CohesionCoupling

W. Stevens, G. Myers, and L. Constantine. Structured design. IBM Systems Journal, 13(2):115 - 139, 1974.



Victor R. Basili, Lionel C. Briand, and Walcélio L. Melo. A validation of object-oriented design metrics as quality indicators. IEEE Transactions on Software

Engineering, 22(10):751 - 761, 1996.

L. C. Briand, J. Wüst, J. W. Daly, and V. 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


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.


Tibor Gyimóthy, Rudolf Ferenc, and István Siket. Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions

on Software Engineering (TSE), 31(10):897 - 910, 2005.

PRODUCTIVITY


2 - 100MAINTENANCE COSTS ARE

GREATER THAN DEVELOPMENT COSTSR.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.sabato 28 settembre 13

HIST: HISTORICAL

INFORMATION FOR SMELL DETECTION

PART II


An AntiPattern is a literary form that describes a commonly occurring solution to a problem that

generates decidedly negative consequencesWilliam 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

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 Object Technology


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 Object Technology


BLOB Feature envy

Divergent change

shotgun surgery

Parallel inheritance hierarchies


HIST PROCESS


HIST PROCESS


EXTRACTING CHANGE HISTORYChanges at method-level are captured

using a code-analyzer developed in the Markos European Project*

* http://markosproject.berlios.desabato 28 settembre 13

HIST PROCESS


HIST PROCESS


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), 2013sabato 28 settembre 13













MINING VERSION HISTORIES THROUGH ASSOCIATION RULE DISCOVERY

C

E

D

B

A

Files

Changes occurring in snapshotsS1 S2 S3 S4 S5 S6 S7 S8

Thomas Zimmermann, Peter Weißgerber, Stephan Diehl, Andreas Zeller : Mining Version Histories to Guide Software Changes. ICSE 2004: 563-572

Annie T. T. Ying, Gail C. Murphy, Raymond T. Ng, Mark Chu-Carroll: Predicting Source Code Changes by Mining

Change History. IEEE Trans. Software Eng. 30(9): 574-586 (2004)

A

C

B

D

A

D

B

D

E

A

C

B

D

E

A

C


MINING VERSION HISTORIES THROUGH ASSOCIATION RULE DISCOVERY

C

E

D

B

A

Files

Changes occurring in snapshotsS1 S2 S3 S4 S5 S6 S7 S8

Thomas Zimmermann, Peter Weißgerber, Stephan Diehl, Andreas Zeller : Mining Version Histories to Guide Software Changes. ICSE 2004: 563-572

Annie T. T. Ying, Gail C. Murphy, Raymond T. Ng, Mark Chu-Carroll: Predicting Source Code Changes by Mining

Change History. IEEE Trans. Software Eng. 30(9): 574-586 (2004)

A

C

B

D

A

D

B

D

E

A

C

B

D

E

A

C


Classes containing at least one method changing together with methods

contained in more than δ (δ=3) different classes


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.



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.





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




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 ofanother class of the system β% (β =70) more

than commits with methods of their class



EMPIRICAL EVALUATIONsabato 28 settembre 13

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 MetricsApache TomcatApache AntJEdit5 API Android

Apache TomcatApache AntJEdit5 API Android

PrecisionRecallF-Measure

PrecisionRecallF-MeasureCorrectti ∩ Correcttj

Correctti \ Correcttj



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


on Automated Software Engineering (ASE’13), 2013

Divergent Change

Shotgun Surgery

Parallel Inheritance

Blob

Feature Envy

F-Measure (Precision - Recall) F-Measure (Precision - Recall)


48 %

41 %

17 %

39 %

35 %

20 %

40 %43 % 17 %

RESULTS


on Automated Software Engineering (ASE’13), 2013

0 % 93 %

100 % 0 %

7 %

0 %

Bad smell HIST ∩ CA HIST \ CA CA \ HISTDivergent Change

Shotgun Surgery

Parallel Inheritance

Blob

Feature Envy


Conclusions & Future work


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


CAN WE DEFINE AN HYBRID APPROACH TO DETECT BAD SMELLS?


CAN WE USE HIST TO DETECT OTHER BAD SMELLS?

Spaghetti CodeLong Method

Complex Class

Refused Bequest

Duplicate Code

Primitive ObsessionMiddle Man

Data Clump


Fabio Palomba fabiopalomba13

[email protected]

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


mailto:[email protected]

mailto:[email protected]

