Cohesion as changeability indicator in object-oriented systems

Kabaili, H.; Keller, Rudolf K.; Lustman, F

doi:10.1109/csmr.2001.914966

Cited by 35 publications

(25 citation statements)

References 9 publications

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“…Furthermore, according to the results of a controlled experiment , static coupling measures may sometimes be inadequate when attempting to explain differences in changeability (e.g., change effort) for object-oriented designs. A follow-up study indicates that the actual flow of messages taking place between objects at run-time is often traced systematically by professional developers when attempting to understand object-oriented software (Kabaili et al, 2001). The results thus suggest that dynamic coupling measures could be of interest as predictors of the cognitive complexity of object-oriented software.…”

Section: Classification Of Coupling Measuresmentioning

confidence: 91%

“…The ultimate goal is to develop predictive models that may be used to support decision making, e.g., decide which classes should undergo more intensive verification and validation. Regardless of the structural attribute considered, most metrics have been so far defined and collected based on a static analysis of the structural attribute considered, most metrics have been so far defined and collected based on a static analysis of external quality attributes, such as fault-proneness (Briand and Labiche, 2002), ripple effects after changes Kabaili et al, 2001) and changeability (Arisholm, 2001;2002;Arisholm et al, 2001, Aly andAbuelnasr, 2010). However, many of the systems that have been studied showed little inheritance and, as a result, limited use of polymorphism and dynamic binding (Deligiannis et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Design Dynamic Coupling Measurement of Distributed Object Oriented Software Using Trace Events

Babu¹

2011

Journal of Computer Science

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Problem statement:A common way to define and measure coupling is through structural properties and static code analysis. However, because of polymorphism, dynamic binding and the common presence of unused code in commercial software, the resulting coupling measures are imprecise as they do not perfectly reflect the actual coupling taking place among classes at run-time. For example, when using static analysis to measure coupling, it is difficult and sometimes impossible to determine what actual methods can be invoked from a client class if those methods are overridden in the subclasses of the server classes. Approach: Coupling measurement has traditionally been performed using static code analysis, because most of the existing work was done on non-object oriented code and because dynamic code analysis is more expensive and complex to perform. We refer to this type of coupling as dynamic coupling. In this study we propose a dynamic and efficient measurement technique over object oriented software. Result: We propose a hybrid model to measure the dynamic coupling present in distributed object oriented software. The proposed method has three steps; they are instrumentation process, post process and coupling measurement. First, the instrumentation process is performed. In this process, to trace method calls, a modified instrumented JVM has been used. During this process, three trace files, .prf, .clp and .svp are created. In the second step, the information present in these files, are merged. At the end of this step, the merged detailed trace of each Jvms contains pointers to the merged trace files of the other JVM's such that the path of each remote call from the client to the server can be uniquely identified. Conclusion: Finally, the coupling metrics are measured dynamically. The proposed system was implemented in JAVA. The implementation results show that the proposed system effectively measures the dynamic coupling.

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Section: Classification Of Coupling Measuresmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Design Dynamic Coupling Measurement of Distributed Object Oriented Software Using Trace Events

Babu¹

2011

Journal of Computer Science

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“…When using PCA, the larger the sample size the better. It has been recommended that an acceptable sample size has ten times the number of observations as there are variables being analyzed [17]. The data set utilized in this research exceeded the suggested ten times number and therefore the results of the principle component analysis are regarded as valid.…”

Section: Principal Component Analysismentioning

confidence: 99%

Empirical Validation of the RCDC and RCDE Semantic Complexity Metrics for Object-Oriented Software

Cox¹,

Gholston²,

Utley³

et al. 2007

CIT

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The Relative Class Domain Complexity (RCDC) and Relative Class Definition Entropy (RCDE) semantic metrics have been proposed for use as complexity metrics for object-oriented software. These semantic metrics are calculated on a knowledge-based representation of software, following a knowledge-based program understanding examination of the software. The metrics have great potential because they can be applied during the software design phase whereas most complexity metrics cannot be applied until after development is complete. In this paper, we present the results of a study to empirically validate the RCDC and RCDE metrics. We show that the metrics compare favorably with the findings of human experts and also that they correlate well with the results of conventional complexity metrics.

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“…The metrics suite proposed by Chidamber and Kemerer is one of the best-known Object-Oriented metrics [27] [28]. Various researchers have conducted empirical studies to validate the Object-Oriented metrics for their effects upon program attributes and quality factors such as development or maintenance effort [7] [20]. Alshayeb and Li predict that Object-Oriented metrics are effective (at least in some cases) in predicting design effort [21].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Application of Package Level Metrics in Assessing Software Quality

Singh¹,

Bhattacherjee²

2012

IJCA

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Today almost all the software industries are overloaded with the maintenance work of already developed software. When any new demand of software arrives, company matches the new problem with the existing product, so that the new product can be easily developed with some new modification in existing products. The reuse of the existing product is only possible when it is measured accurately and efficiently for a longer period.In this paper, first we will calculate the class level metrics viz. CBO, RFC, WMC etc for the entire package then we will calculate the average value for each class level metric (selected) by dividing the value for each metric from the total number of classes for each package. The new resultant metrics are named as CBO avg , RFC avg , WMC avg and so on. The importance of these metrics is to accurately measure the complexity at package level. We then map these package level class metrics with the quality attributes and finally validate these metrics upon three open source projects i.e. Jedit, FreeCS and Llamma chart. Data extraction has been done through an automated tool JHawk and analysis of data has been done using SPSS 10.0 as statistical tool. General TermsSoftware Engineering

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Cohesion as changeability indicator in object-oriented systems

Abstract: Abstract

Cited by 35 publications

References 9 publications

Design Dynamic Coupling Measurement of Distributed Object Oriented Software Using Trace Events

Design Dynamic Coupling Measurement of Distributed Object Oriented Software Using Trace Events

Empirical Validation of the RCDC and RCDE Semantic Complexity Metrics for Object-Oriented Software

Evaluation and Application of Package Level Metrics in Assessing Software Quality

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