a textual-based technique for smell detection

A Textual-based Technique for Smell DetectionFabio Palomba*, Annibale Panichella+, Andrea De Lucia*, Rocco Oliveto°, Andy Zaidman+

* University of Salerno, +Delft University of Technology, ° University of Molise, Italy

ICPC 2016May, 16th 2016Austin, TX, USA

“Bad Code Smells are symptoms of poor design or implementation choices”

[Martin Fowler]

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

[Khomh et al. - EMSE 2012]

Bad Smells increase change- and bug-proneness

[Banker et al. - Comm. of the ACM]Bad Smells increase maintenance costs

To detect code smells, several approaches and tools have

been proposed, most of them relying on structural analysis

N. Moha, Y.-G. Gueheneuc, L. Duchien, and A.-F. L. Meur “DECOR: A Method for the Specification and Detection of Code and Design Smells”

Transactions on Software Engineering (TSE) - Volume 36, Issue 1, 2010

Rule_Card: Long Method {Rule: Metric LOC_METHOD(Very_high, 100)

}

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) - Volume 41, Issue 5, May 2015

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) - Volume 41, Issue 5, May 2015

A class is affected by the Blob code smell when it is involved in more than 8% of the total

commits performed by developers

Structural and Historical Analysis are only a part of the whole story

/* Insert a new user in the system. * @param pUser: the user to insert.*/ public void insert(User pUser){

connect = DBConnection.getConnection();

String sql = "INSERT INTO USER" + "(login,first_name,last_name,password" + ",email,cell,id_parent) " + "VALUES (" + pUser.getLogin() + "," + pUser.getFirstName() + "," + pUser.getLastName() + "," + pUser.getPassword() + "," + pUser.getEMail() + "," + pUser.getCell() + "," + pUser.getIdParent() + ")";

executeOperation(connect, sql); }

/* Delete an user from the system. * @param pUser: the user to delete.*/ public void delete(User pUser) {


String sql = "DELETE FROM USER " + "WHERE id_user = " + pUser.getId();

executeOperation(connect, sql); }

We conjecture that textual content of source code can provide useful hints for smell detection

public void insert(User pUser){



String sql = "DELETE FROM USER " + "WHERE id_user = " + pUser.getId(); }

We believe that code affected by a smell contains unrelated textual content

Insert User

Delete User

TACO

Textual Analysis forCode smell detectiOn

Text Preprocessing

textual component extractor

IR normalizationprocess

StemmingTerm separation

Stop word removal…

Code ComponentCode ComponentCode Component

Smell Detector

BlobLong Method

Promiscuous PackageMisplaced Class

Feature Envy

Detecting Long Method instancespublic void insert(User pUser){




X. Whang, L. Pollock, K. Shanker“Automatic Segmentation of Method Code Into Meaningful Blocks: Design and Evaluation”

JSEP 2013






JSEP 2013

Method CohesionComputation




String sql = "DELETE FROM USER " + "WHERE id_user = " + pUser.getId(); }


JSEP 2013

Method CohesionComputation

Long Method Probability Computation

Detecting Feature Envy instances


C3

method1()method2()

…methodN()

Cclosest

C5C2

C1

C4

Extracting the class Cclosest having the highest textual similarity with Mi

C6


method1()method2()

…methodN()

Cclosest

Feature Envy Probability Computation

method1()method2()

…methodN()

CO

Empirical Study

TACO - Evaluating its performance

10 software systems


10 software systems

Oracle available from Landfill

F. Palomba, D. Di Nucci, M.Tufano, G. Bavota, R. Oliveto, D. Poshyvanyk, A. De Lucia“Landfill: an Open Dataset of Code Smells with Public Evaluation”

MSR 2015


10 software systems

PrecisionRecall

F-Measure


10 software systems


PrecisionRecall

F-Measure


Overlap Metrics

TACO

Alternative Structural Technique

TACO && Alternative

Structural Technique

10 software systems


PrecisionRecall

F-Measure


Feature Envy: TACO vs JDeodorant [Tsantalis et al., TSE 2009]

Blob: TACO vs DECOR [Moha et al., TSE 2010]

Long Method: TACO vs DECOR [Moha et al., TSE 2010]

Promiscuous Package : TACO vs Clustering approach [Girvan et al., NAS 2002]

Misplaced Class: TACO vs AST-based approach [Atkinson et al., APSEC 2005]Overlap

Metrics

TACO - Overall F-Measure

TACOAlternative Structural Technique

+22%on average in terms of F-Measure

TACO - Overall F-Measure

TACO - A practical example Method: findTypesAndPackages()

Goal: Discover the classes and the packages of a given project

Class: CompletionEngine - Eclipse Core

Method: findTypesAndPackages()



65lines of code

TACO - A practical example




65lines of code

A Structural Approach cannot detect the smell!





65lines of code

A Structural Approach cannot detect the smell!

TACO, instead, is able to detect a Long Method instance


TACO - Evaluating the complementarity with structural approaches

TACOAlternative Structural TechniqueOverlap

Textual and Structural Information

are Highly Complementary

What about a combined technique for smell detection?

Do developers perceive textual smells as actual design flaws?

Is TACO able to detect other code smells?

A Textual-based Technique for Smell DetectionFabio Palomba*, Annibale Panichella+, Andrea De Lucia*, Rocco Oliveto°, Andy Zaidman+

* University of Salerno, +Delft University of Technology, ° University of Molise, Italy

ICPC 2016May, 16th 2016Austin, TX, USA