Object-Oriented Reengineering - PowerPoint Presentation

Slide1

Object-Oriented Reengineering Patterns and Techniques

Wahyu Andhyka Kusuma, S.Komkusuma.wahyu.a@gmail.com081233148591

M

ateri 5

Problem Detection

Slide2

Topik

MetricsObject-Oriented Metrics dalam PraktekDuplikasi kode

Slide3

Topik

MetricsKualitas dari Perangkat LunakMenganalisa KecenderunganObject-Oriented Metrics dalam Praktek

Duplikasi k

ode

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7.4

Mengapa menggunakan OO dalam Reengineering?Menaksir kualitas dari perangkat lunakKomponen mana yang memiliki kualitas yang buruk?

(

sehingga dapat di reengineering

)

Komponen yang mana memiliki kualitas yang baik

?

(sehingga dapat di reverse engineered) Metrics sebagai peralatan untuk reengineeringMengontrol proses dari reengineeringMenganalisa kecenderungan : Komponen mana yang bisa diubah??Bagian refactoring mana yang dapat digunakan? Metrics sebagai peralatan reverse engineering!

Slide5

7.5

ISO 9126 Quantitative Quality ModelSoftware

Quality

Functionality

Reliability

Efficiency

Usability

Maintainability

Portability

ISO 9126

Factor

Characteristic

Metric

Error tolerance

Accuracy

Simplicity

Modularity

Consistency

defect density

= #defects / size

correction impact

= #components

changed

correction time

Slide6

7.6Product & Process Attributes

Product Attribute

Definisi

:

Mengukur aspek dari

Hasil yang dikirimkan ke pelanggan

Contoh

:

Jumlah dari sistem

Yang rusak, mempelajari

tentang sistem

Process Attribute

Definisi

:

Mengukur aspek dari

Proses dimana memproduksi produk

Contoh

:

waktu untuk memperbaiki

,

kerusakan jumlah dari komponen

Yang dirubah per perbaikan

Slide7

7.7External & Internal Attributes

External Attribute

Definisi

:

mengukur bagaimana

product/process

berjalan dalam

environment

Contoh

:

waktu rata-rata dalam

kesalahan

,

#components changed

Internal Attribute

Definisi

:

mengukur didalam

Istilah didalam produk Memisahkan FORM, dalam konteks behaviour

Contoh

: class coupling

dan

cohesion, method size

Slide8

7.8

External vs. Internal Product Attributes

External

Internal

Keuntungan

:

close relationship

dengan

quality factors

Kerugian

:

relationship

dengan

quality factors

tidak dalam

empirically validated

Kerugian

:

Mengukur hanya setelah produk digunakan

Pengumpulan data sulit

data

serinkali ada interfrensi pengguna

Menghubungkan eksternal efek ke dalam internal sangat sulit

Keuntungan

:

Dapat diukur kapanpun

Pengumpulan data dapat secara mudah dan otomatis

Berhubungan langsung dengan pengukuran dan penyebabnya

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7.9

Metrik dan PengukuranWeyuker [1988] mendefinisikan sembilan properti dimana Metrik software harus diambilUntuk OO

hanya

6

properti yang sangat penting

[

Chidamber

94, Fenton &

Pfleeger ]Non coarseness:Diberikan sebuah Class P dan sebuak metrik m, kelas lain misal Q juga dapat ditemukan sehingga menjadi m(P)  m(Q)Tidak semua kelas memiliki nilai yang sama untuk metrikNon uniqueness. Dimana kelas P dan Q memiliki ukuran tetap sedemikian sehingga m(P) = m(Q)Dua kelas dapat memiliki metrik yang samaMonotonicitym(P)

 m (P+Q) dan

m(Q)  m (P+Q), P+Q adalah “kombinasi

” dari kelas P dan Q.

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7.10

Metrik dan PengukuranDesign Details are ImportantInti utama dari Class harus mempengaruhi nilai dari metrik. Setiap class melakukan aksi yang sama dengan detailnya harus memberikan dampak terhadap nilai dari metrik.Nonequivalence of Interactionm(P) = m(Q) 

m(P+R) = m(Q+R)

dimana

R

interaksi dengan Class

Interaction Increases Complexity

m(P) + (Q) < m (P+Q).

Dimana dua class digabungkan, interaksi diantaranya juga akan menambah nilai dari metrik

Kesimpulan: Tidak semua pengukuran berupa Metrik

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7.11

Memilih MetrikCepatScalable: Kita tidak dapat menghasilkan log(n2) dimana n



1

juta

LOC

(Line of Code)

Tepat

(

misalnya #methods — perhitungkan semua method, public, juga inherited?)Bergantung pada kodeScalable: Kita menginginkan mengumpulkan metrik dalam waktu samaSederhanaMetrik yang komplek sulit untuk diterjemahkan

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7.12

Menaksir kemudahan perbaikanUkuran dari sistem, termasuk entitas dari sistemUkuran Class, Ukuran method, inheritanceUkuran entitas mempengaruhi maintainability

Kesatuan dari

entities

Class internal

Perubahan harusnya ada dikelas tersebut

Coupling

(penggabungan)

diantara entitasDidalam inheritance: coupling diantara class-subclassDiluar inheritanceStrong coupling mempengarui perubahan di kelas tersebut

Slide13

7.13Sample Size and Inheritance Metrics

Class

Attribute

Method

Access

Invoke

BelongTo

Inherit

Inheritance Metrics

hierarchy nesting level (HNL)

# immediate children (NOC)

# inherited methods, unmodified (NMI)

# overridden methods (NMO)

Class Size Metrics

# methods (NOM)

# instance attributes (NIA, NCA)

# Sum of method size (WMC)

Method Size Metrics

# invocations (NOI)

# statements (NOS)

# lines of code (LOC)

Slide14

7.14Sample class Size

(NIV) [Lore94] Number of Instance Variables (NIV) [Lore94] Number of Class Variables (static) (NCV) [Lore94] Number of Methods (public, private, protected) (NOM)(LOC) Lines of Code(NSC) Number of semicolons [Li93]  number of Statements (WMC) [Chid94] Weighted Method Count

WMC = ∑ c

i

where c is the complexity of a method (number of exit or McCabe Cyclomatic Complexity Metric)

Slide15

7.15Hierarchy Layout

(HNL) [Chid94] Hierarchy Nesting Level , (DIT) [Li93] Depth of Inheritance Tree, HNL, DIT = max hierarchy level(NOC) [Chid94] Number of Children (WNOC) Total number of Children (NMO, NMA, NMI, NME) [Lore94] Number of Method Overridden, Added, Inherited, Extended (super call)(SIX) [Lore94]SIX (C) = NMO * HNL / NOMWeighted percentage of Overridden Methods

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7.16Method Size

(MSG) Number of Message Sends(LOC) Lines of Code(MCX) Method complexity Total Number of Complexity / Total number of methodsAPI calls= 5, Assignment = 0.5, arithmetics op = 2, messages with params = 3....

Slide17

7.17Sample Metrics: Class Cohesion

(LCOM) Lack of Cohesion in Methods [Chidamber 94] for definition[Hitz 95] for critique Ii = set of instance variables used by method Mi

let P = { (Ii, Ij ) | Ii



Ij =

 }

Q = { (Ii, Ij ) | Ii



Ij

  } if all the sets are empty, P is empty LCOM = |P| - |Q| if |P|>|Q| 0 otherwiseTight Class Cohesion (TCC)Loose Class Cohesion (LCC)[Bieman 95] for definitionMeasure method cohesion across invocations

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7.18Sample Metrics: Class Coupling (i)

Coupling Between Objects (CBO)[Chidamber 94a] for definition, [Hitz 95a] for a discussionNumber of other classes to which it is coupled

Data Abstraction Coupling (DAC)

[Li 93] for definition

Number of ADT’s defined in a class

Change Dependency Between Classes (CDBC)

[

Hitz

96a] for definition

Impact of changes from a server class (SC) to a client class (CC).

Slide19

7.19Sample Metrics: Class Coupling (ii)

Locality of Data (LD)[Hitz 96] for definitionLD = ∑ |Li | / ∑ |Ti | Li = non public instance variables + inherited protected of superclass

+ static variables of the class

Ti = all variables used in Mi, except non-static local variables

Mi = methods without accessors

Slide20

7.20The Trouble with Coupling and Cohesion

Coupling and Cohesion are intuitive notionsCf. “computability”E.g., is a library of mathematical functions “cohesive”E.g., is a package of classes that subclass framework classes cohesive? Is it strongly coupled to the framework package?

Slide21

7.21

Conclusion: Metrics for Quality Assessment

Can internal product metrics reveal which components have good/poor quality?

Yes, but...

Not reliable

false positives: “bad” measurements, yet good quality

false negatives: “good” measurements, yet poor quality

Heavyweight Approach

Requires team to develop (customize?) a quantitative quality model

Requires definition of thresholds (trial and error)

Difficult to interpret

Requires complex combinations of simple metrics

However...

Cheap once you have the quality model and the thresholds

Good focus (± 20% of components are selected for further inspection)

Note: focus on the most complex components first!

Slide22

Topik

MetricsObject-Oriented Metrics dalam PraktekDetection strategies, filters and compositionSample detection strategies: God Class …Duplikasi k

ode

Slide23

7.23Detection strategy

A detection strategy is a metrics-based predicate to identify candidate software artifacts that conform to (or violate) a particular design rule

Slide24

7.24Filters and composition

A data filter is a predicate used to focus attention on a subset of interest of a larger data setStatistical filtersI.e., top and bottom 25% are considered outliersOther relative thresholdsI.e., other percentages to identify outliers (e.g., top 10%)

Absolute thresholds

I.e., fixed criteria, independent of the data set

A useful detection strategy can often be expressed as a

composition

of data filters

Slide25

7.25God Class

A God Class centralizes intelligence in the system

Impacts understandibility

Increases system fragility

Slide26

7.26Feature Envy

Methods that are more interested in data of other classes than their own [Fowler et al. 99]

Slide27

7.27Data Class

A Data Class provides data to other classes but little or no functionality of its own

Slide28

7.28Data Class (2)

Slide29

7.29Shotgun Surgery

A change in an operation implies many (small) changes to a lot of different operations and classes

Slide30

Topik

MetricsObject-Oriented Metrics dalam PraktekDuplikasi kodeDetection techniques

Visualizing duplicated code

Slide31

7.31

Kode di salinContoh dari Mozilla Distribution (Milestone 9)Diambil dari /dom

/

src

/base/nsLocation.cpp

Slide32

7.32

Contoh

LOC

Duplikasi tanpa komentar

Dengan komentar

gcc

460’000

8.7%

5.6%

Database Server

245’000

36.4%

23.3%

Payroll

40’000

59.3%

25.4%

Message Board

6’500

29.4%

17.4%

Berapa banyak kode diduplikasi?

Biasanya diperkirakan

:

8

hingga

12%

dari kode

Slide33

7.33

Apa itu duplikasi kode?Duplikasi kode

=

Bagian dari kode program ditemukan ditempat lain dalam satu sistem yang sama

Dalam File yang berbeda

Dalam File sama tapi Method berbeda

Dalam Method yang sama Bagian tersebut harus memiliki logika atau struktur yang sama sehingga dapat diringkas,

Slide34

7.34

Permasalahan dari duplikasiBiasanya memberikan efek negatifPenggelembungan kodeEfek negatif ketika perbaikan sistem atau softwareMenyalin menjadi kerusakan tambahan dalam kode

Software

Aging, “hardening of the arteries”,

“Software Entropy” increases even small design changes become very difficult to effect

Slide35

7.35

Nontrivial problem: No a priori knowledge about which code has been copied How to find all clone pairs among all possible pairs of segments?

Mendeteksi duplikasi kode

Slide36

7.36

Author

Level

Transformed Code

Comparison Technique

Johnson 94

Lexical

Substrings

String-Matching

Ducasse 99

Lexical

Normalized Strings

String-Matching

Baker 95

Syntactical

Parameterized Strings

String-Matching

Mayrand 96

Syntactical

Metric Tuples

Discrete comparison

Kontogiannis 97

Syntactical

Metric Tuples

Euclidean distance

Baxter 98

Syntactical

AST

Tree-Matching

General Schema of Detection Process

Slide37

7.37Recall and Precision

Slide38

7.38

…//assign same fastid as containerfastid = NULL;const char* fidptr = get_fastid();if(fidptr != NULL) {

int l = strlen(fidptr);

fastid = newchar[ l + 1 ];

…

fastid=NULL;

constchar*fidptr=get_fastid();

if(fidptr!=NULL)

intl=strlen(fidptr)

fastid = newchar[l+]Simple Detection Approach (i) Assumption: Code segments are just copied and changed at a few places Noise elimination transformation remove white space, comments remove lines that contain uninteresting code elements (e.g., just ‘else’ or ‘}’)

Slide39

7.39Simple Detection Approach (ii)

Code Comparison StepLine based comparison (Assumption: Layout did not change during copying)Compare each line with each other line. Reduce search space by hashing: Preprocessing: Compute the hash value for each line Actual Comparison: Compare all lines in the same hash bucketEvaluation of the Approach

Advantages: Simple, language independent

Disadvantages: Difficult interpretation

Slide40

7.40

A Perl script for C++ (i)

Slide41

7.41A Perl script for C++ (ii)

Handles multiple files

Removes comments

and white spaces

Controls noise (if, {,)

Granularity (number of lines)

Possible to remove keywords

Slide42

7.42Output Sample

Lines: create_property(pd,pnImplObjects,stReference,false,*iImplObjects);create_property(pd,pnElttype,stReference,true,*iEltType);

create_property(pd,pnMinelt,stInteger,true,*iMinelt);

create_property(pd,pnMaxelt,stInteger,true,*iMaxelt);

create_property(pd,pnOwnership,stBool,true,*iOwnership);

Locations: </face/typesystem/SCTypesystem.C>6178/6179/6180/6181/6182

</face/typesystem/SCTypesystem.C>6198/6199/6200/6201/6202

Lines:

create_property(pd,pnSupertype,stReference,true,*iSupertype);

create_property(pd,pnImplObjects,stReference,false,*iImplObjects);create_property(pd,pnElttype,stReference,true,*iEltType);create_property(pd,pMinelt,stInteger,true,*iMinelt);create_property(pd,pnMaxelt,stInteger,true,*iMaxelt);Locations: </face/typesystem/SCTypesystem.C>6177/6178</face/typesystem/SCTypesystem.C>6229/6230Lines = duplicated linesLocations = file names and line number

Slide43

7.43Enhanced Simple Detection Approach

Code Comparison StepAs before, but nowCollect consecutive matching lines into match sequencesAllow holes in the match sequenceEvaluation of the ApproachAdvantagesIdentifies more real duplication, language independentDisadvantagesLess simple

Misses copies with (small) changes on every line

Slide44

7.44Abstraction

Abstracting selected syntactic elements can increase recall, at the possible cost of precision

Slide45

7.45Metrics-based detection strategy

Duplication is significant if:It is the largest possible duplication chain uniting all exact clones that are close enough to each other.

The duplication is large enough.

Slide46

7.46Automated detection in practice

Wettel [ MSc thesis, 2004] uses three thresholds:Minimum clone length: the minimum amount of lines present in a clone (e.g., 7)Maximum line bias: the maximum amount of lines in between two exact chunks (e.g., 2)Minimum chunk size: the minimum amount of lines of an exact chunk (e.g., 3)

Mihai Balint, Tudor Gîrba and Radu Marinescu,

“How Developers Copy,”

ICPC 2006

Slide47

7.47

Visualization of Duplicated CodeVisualization provides insights into the duplication situation

A simple version can be implemented in three days

Scalability issue

Dotplots — Technique from DNA Analysis

Code is put on vertical as well as horizontal axis

A match between two elements is a dot in the matrix

Slide48

7.48

Detected ProblemFile A contains two copies of a piece of codeFile B contains another copy of this codePossible SolutionExtract MethodAll examples are made using Duploc from an industrial case study (1 Mio LOC C++ System)

Visualization of Copied Code Sequences

Slide49

7.49

Detected Problem4 Object factory clones: a switch statement over a type variable is used to call individual construction codePossible Solution

Strategy Method

Visualization of Repetitive Structures

Slide50

7.50Visualization of Cloned Classes

Class A

Class B

Class B

Class A

Detected Problem:

Class A is an edited copy

of class B. Editing & Insertion

Possible Solution

Subclassing …

Slide51

7.51

20 Classes implementing lists for different data types

Detail

Overview

Visualization of Clone Families

Slide52

7.52

KesimpulanDuplikasi Kode adalah masalah nyataMembuat sistem semakin susah untuk diubahMendeteksi duplikasi kode adalah masalah beratBeberapa teknik sederhana dapat membantuDukungan dari alat lain juga dibutuhkan

Visualisasi dari kode sangat berguna

Mengatasi duplikasi kode bisa dijadikan bahan penelitian