Chidamber And Kemerer Metrics Research Articles

Predicting Reliability of Object-Oriented Systems Using a Neural Network

One of the most important quality attributes of computer systems is reliability, which addresses the ability of the software to perform its required function under stated conditions for a stated period of time. The paper aim is twofold. Firstly, the proposed approach explores how to define a metric to qualify the sub-aspects comprised in ISO 25010 regarding reliability as maturity and availability. Secondly, we investigate to what extent the internal structure of the system quantified by the Chidamber and Kemerer (CK) metrics may be used to predict reliability. The approach for prediction is a feed-forward neural network with back-propagation learning. The results indicate that CK metrics are promising in predicting reliability using a neural network method.

QUALITY OF SOFTWARE PROJECTS – A CASE STUDY

In software projects, many points that are overlooked such as time constraints and human factors are causing great problems in the future. By measuring the quality of software projects, problems that may arise in important parameters such as maintenance-repair, functionality and reliability can be eliminated. In this study, metrics that can be used for measuring maintainability quality attribute within the scope of ISO 9126 Quality Standard are examined. In order to perform the study, 40 open source object-oriented software was selected and code complexity analysis was performed. Values of metric sets such as Chidamber and Kemerer (CK), Lorenz and Kidd (LK) and McCabe's complex Suite were determined by the Understand Code Analysis tool. It was determined whether the obtained values exceeded the threshold values indicated in the literature. Frequencies of metrics passing threshold values were determined for 40 open source object-oriented software projects, and the consistency among the metrics was evaluated using WEKA Machine Learning Software and EXCEL Data Analysis Tool. When the results were evaluated, it was observed that in addition to CK metrics such as WMC, CBO, and RFC, which measure the maintainability quality attribute, NOC (CK), NIM (LK), and the ratio of comment/code metrics have been observed to yield significant measurement results.

Experimental Validation of Inheritance Metrics’ Impact on Software Fault Prediction

Software faults can cause trivial annoyance to catastrophic failures. Recent work in software fault prediction (SFP) advocates the need for predicting faults before deployment to aid testing process. Object-oriented programming is complex while comparing it with procedural languages having multiple dimensions wherein inheritance is an important aspect. In this paper, we aim to investigate how much inheritance metrics assist in predicting software fault proneness. We first select the Chidamber and Kemerer (CK) metrics, most accepted metric suite for predicting software faults and inheritance metrics. We use 65 publicly available base datasets having CK metrics and some other inheritance metrics to evaluate the impact of inheritance on SFP. We split each dataset into further two datasets: inheritance with CK and CK without inheritance for comparison of results. An artificial neural network (ANN) is used for model building, and accuracy, recall, precision, F1 measures, and true negative rate (TNR) are used for measuring performance. Comparison is made and the results show an acceptable contribution of inheritance metrics in SFP. The testing community can safely use inheritance metrics in predicting software faults. Moreover, high inheritance is not desirable, as this can potentially lead to software faults.

McCabe’s Complexity and CK Metrics on the Internal Quality of Test First Implementation in Malaysian Education Settings

Test first is promoted in test driven development method as one of an effective Agile manifesto in producing a better quality applications. Several research have been conducted in education settings and among industrial practitioners in order to investigate the test first contribution in producing better quality software compared to a traditional approach. This paper focuses on studying the internal quality of the project developed by undergraduates with the implementation of test first over test last approach in Malaysian education settings. In the analyses, JHawk is used as the metrics extraction tools, and the analysis utilized the SPSS and G*Power statistical packages. The metrics collected are based on six object oriented metrics by Chidamber and Kemerer (CK) and the McCabe’s cyclomatic complexity (CC). However, only four CK Metric (Lack of Cohesion in Method, Coupling between Objects, Weighted Methods per Class, and Response for a Class) were evaluated, in addition, the complexity is measured based on McCabes’s CC. The outcome based on t-test and Mann-Whitney test shows that none of the metrics is statistically significant for test first in producing better internal quality; however, the hypothesis is accepted due to the effect size and achieved power contributed by the Weighted Method per Class.

Utility of CK Metrics in Predicting Size of Board-Based Software Games

Software size is one of the most important inputs of many software cost and effort estimation models. Early estimation of software plays an important role at the time of project inception. An accurate estimate of software size is, therefore, crucial for planning, managing, and controlling software development projects dealing with the development of software games. However, software size is unavailable during early phase of software development. This research determines the utility of CK (Chidamber and Kemerer) metrics, a well-known suite of object-oriented metrics, in estimating the size of software applications using the information from its UML (Unified Modeling Language) class diagram. This work focuses on a small subset dealing with board-based software games. Almost sixty games written using an object-oriented programming language are downloaded from open source repositories, analyzed and used to calibrate a regression-based size estimation model. Forward stepwise MLR (Multiple Linear Regression) is used for model fitting. The model thus obtained is assessed using a variety of accuracy measures such as MMRE (Mean Magnitude of Relative Error), Prediction of x(PRED(x)), MdMRE (Median of Relative Error) and validated using K-fold cross validation. The accuracy of this model is also compared with an existing model tailored for size estimation of board games. Based on a small subset of desktop games developed in various object-oriented languages, we obtained a model using CK metrics and forward stepwise multiple linear regression with reasonable estimation accuracy as indicated by the value of the coefficient of determination (R 2 = 0.756).Comparison results indicate that the existing size estimation model outperforms the model derived using CK metrics in terms of accuracy of prediction.

A Classification of Software Modules into Library and Application Components in the Open-Source Field

Software reuse significantly reduce the costs of software production in the field of open-source software (OSS) development and lead to produce more reliable software systems. Software metrics have been proposed as indicators of software quality factors such as reusability. However, few empirical research papers have validated the relationship between components reusability and software metrics. This research aims to validate Chidamber and Kemerer (CK) metrics as predictors of software reusability. In order to achieve this goal, an empirical study is conducted to validate metrics in classifying two groups of components: library (reuse-prone) and non-library (less reuseprone). A nearest neighbor’s technique is used to classify library and application components using object-oriented software metrics. The approach is applied to a number of library and application systems available online. The conducted nearest neighbors models have produced acceptable classification. The results provide evidence of using metrics as surrogates of software reusability when models are evaluated using Fmeasure. CK metrics can be used to measure component reuse-proneness and can be used to differentiate between library and application components. A nearest neighbor’s technique can be used to identify the reuse-prone components in open-source application.

The Analysis of Object-Oriented Metrics in C++ Programs

Software metrics play a very important role to develop good quality software. In today's software development, object-oriented (OO) languages are used due to their basic features like class, object, information hiding, inheritance, encapsulation, abstraction and polymorphism. In addition, a number of object-oriented metrics are available to be used to measure the quality of the OO systems. One of the most popular OO metrics suite is the Chidamber and Kemerer (CK) metrics suite. The main focus of this research is to apply the CK metrics to a group of open source systems written in C++. The results show that only two out of six CK metrics, NOC and RFC are significant in predicting defects in the systems. Index Terms—Object-oriented language, software metrics, C++, CK metrics, open source system. 

Software Reusability Classification and Predication Using Self-Organizing Map (SOM)

Due to rapid development in software industry, it was necessary to reduce time and efforts in the software development process. Software Reusability is an important measure that can be applied to improve software development and software quality. Reusability reduces time, effort, errors, and hence the overall cost of the development process. Reusability prediction models are established in the early stage of the system development cycle to support an early reusability assessment. In Object-Oriented systems, Reusability of software components (classes) can be obtained by investigating its metrics values. Analyzing software metric values can help to avoid developing components from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order to identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to cluster datasets of CK metrics values that were extracted from three different java-based systems. The goal was to find the relationship between CK metrics values and the reusability level of the class. The reusability level of the class was classified into three main categorizes (High Reusable, Medium Reusable and Low Reusable). The clustering was based on metrics threshold values that were used to achieve the experiments. The proposed methodology succeeds in classifying classes to their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments show how SOM can be applied on software CK metrics with different sizes of SOM grids to provide different levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and Number of Children (NOC) metrics dominated the clustering process, so these two metrics were discarded from the experiments to achieve a successful clustering. The most efficient SOM topology [2 × 2] grid size is used to predict the reusability of classes.

Empirical evidence on OCL formal specification-based metrics as a predictor of fault-proneness

OCL formal specification has been widely used for precise software modeling. In general, it is used to express constraints on design documents. As a novel approach, its usage can be extended to support effective testing, such as testing fault-prone components to improve software quality. In this paper, CK (Chidamber and Kemerer) metrics that can be extracted from OCL expressions are validated against module complexity. Moreover, our study proposes a new metric suite that can be directly extracted from OCL expressions to quantify module complexity. A weight has been assigned to each metric to reflect its importance in fault-prone component identification. Our study shows that metrics collected from OCL specifications are good predictors of the fault-prone components of a system during design time. An early knowledge of fault-prone components of the system can be useful to distribute efforts required for software development and testing activities.

Cross Company and within Company Fault Prediction using Object Oriented Metrics

paper investigates fault predictions in the cross-project context focusing on the object oriented metrics for the organizations that do not track fault related data. In this study, empirical analysis is carried out to validate object-oriented Chidamber and Kemerer (CK) design metrics for cross project fault prediction. The machine learning techniques used for evaluation are J48, NB, SVM, RF, K-NN and DT. The results indicate CK metrics can be used as initial guideline for the projects where no previous fault data is available. Overall, the results of cross company is comparable to the within company data learning. Our analysis is in favour of reusability in object oriented technology and it has been empirically shown that object oriented metric data can be used for cross company fault prediction in initial stage when previous fault data of the project is not available. Keywordsprediction, cross company, Software metric, open source software.

Evaluation of the Design Metric to Reduce the Number of Defects in Software Development

Software design is one of the most important and key activities in the system development life cycle (SDLC) phase that ensures the quality of software. Different key areas of design are very vital to be taken into consideration while designing software. Software design describes how the software system is decomposed and managed in smaller components. Object-oriented (OO) paradigm has facilitated software industry with more reliable and manageable software and its design. The quality of the software design can be measured through different metrics such as Chidamber and Kemerer (CK) design metrics, Mood Metrics & Lorenz and Kidd metrics. CK metrics is one of the oldest and most reliable metrics among all metrics available to software industry to evaluate OO design. This paper presents an evaluation of CK metrics to propose an improved CK design metrics values to reduce the defects during software design phase in software. This paper will also describe that whether a significant effect of any CK design metrics exists on total number of defects per module or not. This is achieved by conducting survey in two software development companies.

A Quantitative Investigation of the Acceptable Risk Levels of Object-Oriented Metrics in Open-Source Systems

Object-oriented metrics have been validated empirically as measures of design complexity. These metrics can be used to mitigate potential problems in the software complexity. However, there are few studies that were conducted to formulate the guidelines, represented as threshold values, to interpret the complexity of the software design using metrics. Classes can be clustered into low and high risk levels using threshold values. In this paper, we use a statistical model, derived from the logistic regression, to identify threshold values for the Chidamber and Kemerer (CK) metrics. The methodology is validated empirically on a large open-source system-the Eclipse project. The empirical results indicate that the CK metrics have threshold effects at various risk levels. We have validated the use of these thresholds on the next release of the Eclipse project-Version 2.1-using decision trees. In addition, the selected threshold values were more accurate than those were selected based on either intuitive perspectives or on data distribution parameters. Furthermore, the proposed model can be exploited to find the risk level for an arbitrary threshold value. These findings suggest that there is a relationship between risk levels and object-oriented metrics and that risk levels can be used to identify threshold effects.