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Chidamber & Kemerer Chidamber & Kemerer Suite of MetricsSuite of Metrics

Camargo Cruz Ana Erika

Supervisor: Ochimizu Koichiro

May 2008

Japan Advanced Institute of Science and TechnologyJapan Advanced Institute of Science and TechnologySchool of Information Science School of Information Science

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CK Metrics: Outline

• Objective

• Definition & Guidelines

• Thresholds

• CK in the literature (other uses)

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CK Metrics: Objective

CK metrics were designed [1]:

• To measure unique aspects of the OO approach.

• To measure complexity of the design.

• To improve the development of the software

HOW?

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CK Metrics: ObjectiveSW development Improvement

Managers can improve the development of the SW by :

• Analysing CK metrics through the identification of outlying values (extreme deviations), which may be a signal of:– high complexity and/or

– possible design violations

• Taking managerial decisions, such as: Re-designing and/or assigning extra or higher

skilled resources (to develop, to test and to maintain the SW).

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CK Metrics: DefinitionWMC (Weighted Methods per Class)

• Definition– WMC is the sum of the complexity of the methods of a class.– WMC = Number of Methods (NOM), when all method’s

complexity are considered UNITY.

• Viewpoints

– WMC is a predictor of how much TIME and EFFORT is required to develop and to maintain the class.

– The larger NOM the greater the impact on children.

– Classes with large NOM are likely to be more application specific, limiting the possibility of RE-USE and making the EFFORT expended one-shot investment.

• Objective: Low

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CK Metrics: DefinitionDIT (Depth of Inheritance Tree)

• DefinitionThe maximum length from the node to the root of the tree

• ViewpointsThe greater values of DIT :– The greater the NOM it is likely to inherit, making more

COMPLEX to predict its behaviour– The greater the potential RE-USE of inherited methods

Small values of DIT in most of the system’s classes may be an indicator that designers are forsaking RE-USABILITY for simplicity of UNDERSTANDING.

• Objective: Trade-off

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CK Metrics: DefinitionNOC (Number of Children)

• Definition Number of immediate subclasses subordinated to a class in the class hierarchy

• ViewpointsThe greater the NOC is:– the greater is the RE-USE– the greater is the probability of improper abstraction of the

parent class,– the greater the requirements of method's TESTING in that class.

Small values of NOC, may be an indicator of lack of communication between different class designers.

• Objective: Trade-off

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CK Metrics: DefinitionCBO (Coupling Between Objects)

• DefinitionIt is a count of the number of other classes to which it is coupled

• Viewpoints

Small values of CBO :– Improve MODULARITY and promote ENCAPSULATION– Indicates independence in the class, making easier its RE-USE– Makes easier to MAINTAIN and to TEST a class.

• Objective: Low

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CK Metrics: DefinitionRFC (Response for Class)

• DefinitionIt is the number of methods of the class plus the number of methods

called by any of those methods.

• Viewpoints If a large numbers of methods are invoked from a class (RFC is

high):– TESTING and MAINTANACE of the Class becomes more

COMPLEX.

• Objective: Low

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CK Metrics: DefinitionLCOM (Lack of Cohesion of Methods)

• DefinitionMeasures the dissimilarity of methods in a class via instanced variables.

• ViewpointsGreat values of LCOM:

– Increases COMPLEXITY– Does not promotes ENCAPSULATION and implies classes

should probably be split into two or more subclasses– Helps to identified low-quality design

• Objective: Low

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CK Metrics: Guidelines

But How much is Low and High ?

METRIC GOAL LEVEL COMPLEXITY

(To develop, to test and to maintain)

RE-USABILITY ENCAPSULATION, MODULARITY

WMC Low ▼ ▼ ▲

DIT Trade-off ▼ ▼ ▼

▲ ▲ ▲

NOC Trade-off ▼ ▼ ▼

▲ ▲ ▲

CBO Low ▼ ▼ ▲

RFC Low ▼ ▼

LCOM Low ▼ ▼ ▲

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CK Metrics: Thresholds

Thresholds of the CK metrics [2,3,4]:

• Can not be determined before their use

• Should be derived and use locally for each dataset

• 80th and 20th percentiles of the distributions can be used to determine high and low values of the metrics.

• Are not indicators of “badness” but indicators of difference that needs to be investigated.

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CK in the Literature CK Metrics & other Managerial performance indicators

Chidamber & Kemerer study the relation of CK metrics with [2]:

• Productivity

SIZE [LOC] / EFFORT of Development [Hours]

• Rework Effort for re-using classes

• Effort to specify high-level design of classes

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CK in the Literature CK Metrics & Maintenance effort

Li and Henry (1993) use CK metrics (among others) to predict [5]:

• Maintenance effort, which is measured by the number of lines that have changed in a class during 3 years that they have collected the measurement .

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CK in the Literature DIT & Maintenance effort

Daly et al. (1996) in his study concludes that[5]:

• That subjects maintainig OO SW with three levels

of inheritance depth performed maintaince tasks

significantly quickier than those maintaining an

equivalent OO SW with no inheritance.

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CK in the Literature DIT & Maintenance effort

However, Hand Harrisson (2000) used DIT metric to demonstrate [5]:

• That systems without inheritance are easier to

understand and modify than systems with 3 or 5

levels of inheritance.

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CK in the Literature DIT & Maintenance effort

Poels (2001) uses DIT metric, and demonstrate [5]:

• The extensive use of inheritance leads to modls

that are more difficult to modify.

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CK in the Literature DIT & Maintenance effort

Prechelt (2003) concludes that [5]:

• Programs with less inheritance were faster to

maintain and

• The code maintenance effort is hardly correlated

with inheritance depth but rather depends on

other factors such as number of relevant

methods.

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CK in the LiteratureCK Metrics & Fault-proneness prediction

Study Input: Design Complexity Metrics

Output Prediction Technique

1996

Basili et al. [6]

CK Metrics among others

Fault-prone classes Multivariate Logistic Regression

2000

Briand et al.[7]

Fault-prone classes Multivariate Logistic Regression

2004

Kanmani et al.[8]

Fault ratio General Regression Neural Network

2005

Nachiappan et al.[9]

Fault ratio Multiple Linear Regression

2007

Olague et al.[10] CK, QMOOD

Fault-prone classes Multivariate Logistic Regression

CK : Chidamber & Kemerer, QMOOD: Quality Metrics for Object Oriented Design

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Conclusion

• CK metrics measure complexity of the design

• There are no thresholds defined for the CK

metrics. However, they can be used identifying

outlaying values.

• CK metrics (while measure from the code) have

been related to: fault-proneness, productivity,

rework effort, design effort and maintenance.

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References[1] Chidamber Shyam, Kemerer Chris, “A metrics suite for object oriented

design”, IEEE Transactions on Software Engineering, June1994.

[2] Chidamber Shyam, Kemerer Chris, Darcy David, ”Managerial use of Metrics

for Object-Oriented Software: an Exploratory Analysis”, IEEE Transactions

on software Engineering, August 1998.

[3] Linda Rosenberg, “Applying and Interpreting Object Oriented Metrics”,

Software Assurance Technology Conference, Utah, 1998.

[4] Stephen H. Kan, “Metrics and models in software Quality Engineering”,

Addison-Wesley, 2003.

[5] Genaros Marcela, Piattini Mario, Calero Coral, “A Survey of Metrics for UML

Class Diagrams”, Journal of Object Technology, Nov.-Dec 2005.

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References[6] Victor R. Basili and Lionel C. Briand and Walcelio L. Melo, A Validation of Object-

Oriented Design Metrics as Quality Indicators, IEEE Transactions on Software engineering, Piscataway, NJ, USA, October 1996.

[7] Lionel C. Briand and Jurgen Wust and John W. Daly and D. Victor Porter, Exploring � �the relationships between design measures and software quality in object-oriented systems Journal of Systems and Software,2000.

[8] Kanmani, S., and Uthariaraj V. Rymend, Object oriented software quality prediction using general regression neural networks, SIGSOFT Soft. Eng. Notes, New York NY, USA, 2004.

[9] Nachiappan Nagappan, and Williams Laurie, Early estimation of software quality using in-process testing metrics: a controlled case study , Proceedings of the third workshop on Software quality, St. Louis, Missouri, USA. (2005)

[10] Hector M. Olague and Sampson Gholston and Stephen Quattlebaum, Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes, IEEE Transactions Software Engineering, Piscataway, NJ, USA, 2007.