Chidamber & Kemerer Suite of Metrics Japan Advanced Institute of Science and Technology School of Information Science Chidamber & Kemerer Suite of Metrics Camargo Cruz Ana Erika Supervisor: Ochimizu Koichiro May 2008

CK Metrics: Outline Objective Definition & Guidelines Thresholds CK in the literature (other uses)

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?

CK Metrics: Objective SW 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).

CK Metrics: Definition WMC (Weighted Methods per Class) 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

CK Metrics: Definition DIT (Depth of Inheritance Tree) The maximum length from the node to the root of the tree Viewpoints The 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

CK Metrics: Definition NOC (Number of Children) Number of immediate subclasses subordinated to a class in the class hierarchy Viewpoints The 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

CK Metrics: Definition CBO (Coupling Between Objects) It 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

CK Metrics: Definition RFC (Response for Class) It 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

CK Metrics: Definition LCOM (Lack of Cohesion of Methods) Measures the dissimilarity of methods in a class via instanced variables. Viewpoints Great 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

CK Metrics: Guidelines GOAL LEVEL COMPLEXITY (To develop, to test and to maintain) RE-USABILITY ENCAPSULATION, MODULARITY WMC Low ▼ ▲ DIT Trade-off NOC CBO RFC LCOM But How much is Low and High ?

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.

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

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 .

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.

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.

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.

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.

CK in the Literature CK 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] 2004 Kanmani et al.[8] Fault ratio General Regression Neural Network 2005 Nachiappan et al.[9] Multiple Linear Regression 2007 Olague et al.[10] CK, QMOOD CK : Chidamber & Kemerer, QMOOD: Quality Metrics for Object Oriented Design

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.

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.

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.