On the applicability of Weyuker Property 9 to object-orientedstructural inheritance complexity metrics

In the metric suite for object oriented design put forward by S.R. Chidamber and C.F. Kemerer (1994), it is observed that E. Weyuker's (1988) Property 9 is not satisfied by any of the structural inheritance complexity metrics. The same is also observed for candidate structural inheritance complexity metrics proposed by A.F. Brito and R. Carapuca (1994). The authors formally show that particular classes of inheritance metrics (that include the above proposals) that are defined on a directed graph abstraction of the inheritance structure and that are contrived on the assumptions and definitions given by Chidamber and Kemerer, can never satisfy Property 9. Furthermore, it is also argued that the formalisation can be generalized to include other classes of structural metrics that are not necessarily inheritance metrics     

A validation of object-oriented design metrics as qualityindicators

This paper presents the results of a study in which we empirically investigated the suite of object-oriented (OO) design metrics introduced in (Chidamber and Kemerer, 1994). More specifically, our goal is to assess these metrics as predictors of fault-prone classes and, therefore, determine whether they can be used as early quality indicators. This study is complementary to the work described in (Li and Henry, 1993) where the same suite of metrics had been used to assess frequencies of maintenance changes to classes. To perform our validation accurately, we collected data on the development of eight medium-sized information management systems based on identical requirements. All eight projects were developed using a sequential life cycle model, a well-known OO analysis/design method and the C++ programming language. Based on empirical and quantitative analysis, the advantages and drawbacks of these OO metrics are discussed. Several of Chidamber and Kemerer's OO metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. Also, on our data set, they are better predictors than “traditional” code metrics, which can only be collected at a later phase of the software development processes     

The confounding effect of class size on the validity ofobject-oriented metrics

Much effort has been devoted to the development and empirical validation of object-oriented metrics. The empirical validations performed thus far would suggest that a core set of validated metrics is close to being identified. However, none of these studies allow for the potentially confounding effect of class size. We demonstrate a strong size confounding effect and question the results of previous object-oriented metrics validation studies. We first investigated whether there is a confounding effect of class size in validation studies of object-oriented metrics and show that, based on previous work, there is reason to believe that such an effect exists. We then describe a detailed empirical methodology for identifying those effects. Finally, we perform a study on a large C++ telecommunications framework to examine if size is really a confounder. This study considered the Chidamber and Kemerer metrics and a subset of the Lorenz and Kidd metrics. The dependent variable was the incidence of a fault attributable to a field failure (fault-proneness of a class). Our findings indicate that, before controlling for size, the results are very similar to previous studies. The metrics that are expected to be validated are indeed associated with fault-proneness     

Chidamber and Kemerer's metrics suite: a measurement theoryperspective

The metrics suite for object-oriented design put forward by Chidamber and Kemerer (1994) is partly evaluated by applying principles of measurement theory. Using the object coupling measure (CBO) as an example, it is shown that failing to establish a sound empirical relation system can lead to deficiencies of software metrics. Similarly, for the object-oriented cohesion measure (LCOM) it is pointed out that the issue of empirically testing the representation condition must not be ignored, even if other validation principles are carefully obeyed. As a by-product, an alternative formulation for LCOM is proposed     

An Empirical Exploration of the Distributions of the Chidamber and Kemerer Object-Oriented Metrics Suite

The object-oriented metrics suite proposed by Chidamber and Kemerer (CK) is a measurement approach towards improved object-oriented design and development practices. However, existing studies evidence traces of collinearity between some of the metrics and low ranges of other metrics, two facts which may endanger the validity of models based on the CK suite. As high correlation may be an indicator of collinearity, in this paper, we empirically determine to what extent high correlations and low ranges might be expected among CK metrics.To draw as much general conclusions as possible, we extract the CK metrics from a large data set (200 public domain projects) and we apply statistical meta-analysis techniques to strengthen the validity of our results. Homogenously through the projects, we found a moderate (0.50) to high correlation (>0.80) between some of the metrics and low ranges of other metrics.Results of this empirical analysis supply researchers and practitioners with three main advises: a) to avoid the use in prediction systems of CK metrics that have correlation more than 0.80 b) to test for collinearity those metrics that present moderate correlations (between 0.50 and 0.60) c) to avoid the use as response in continuous parametric regression analysis of the metrics presenting low variance. This might therefore suggest that a prediction system may not be based on the whole CK metrics suite, but only on a subset consisting of those metrics that do not present either high correlation or low ranges.     

Mining the impact of evolution categories on object-oriented metrics

Despite the relevance of the software evolution phase, there are few characterization studies on recurrent evolution growth patterns and on their impact on software properties, such as coupling and cohesion. In this paper, we report a study designed to investigate whether the software evolution categories proposed by Lanza can be used to explain not only the growth of a system in terms of lines of code (LOC), but also in terms of metrics from the Chidamber and Kemerer (CK) object-oriented metrics suite. Our results show that high levels of recall (ranging on average from 52 to 72 %) are achieved when using LOC to predict the evolution of coupling and size. For cohesion, we have achieved smaller recall rates (<27 % on average).     

Identification of defect-prone classes in telecommunication software systems using design metrics

The goal of this paper is to investigate the relation between object-oriented design choices and defects in software systems, with focus on a real-time telecommunication domain. The design choices are measured using the widely accepted metrics suite proposed by Chidamber and Kemerer for object oriented languages [S.R. Chidamber, C.F. Kemerer, A metrics suite for object oriented design, IEEE Transactions on Software Engineering 20 (6) (1994) 476-493].This paper reports the results of an extensive case study, which strongly reinforces earlier, mainly anecdotal, evidence that design aspects related to communication between classes can be used as indicators of the most defect-prone classes.Statistical models applicable for the non-normally distributed count data are used, such as Poisson regression, negative binomial regression, and zero-inflated negative binomial regression. The performances of the models are assessed using correlations, dispersion coefficients and Alberg diagrams.The zero-inflated negative binomial regression model based on response for a class shows the best overall ability to describe the variability of the number of defects in classes.     

Empirical evaluation of reuse sensitiveness of complexity metrics

Measuring software products and processes is essential for improving software productivity and quality. In order to evaluate the complexity of object-oriented software, several complexity metrics have been proposed. Among them, Chidamber and Kemerer’s metrics are the most well-known for object-oriented software. Their metrics evaluate the complexity of the classes in terms of internal, inheritance, and coupling complexity. Though the reused classes of the class library usually have better quality than the newly-developed ones, their metrics deal with inheritance and coupling complexity in the same way. This article first proposes a revision of the Chidamber and Kemerer’s metrics which can be applied to software which had been constructed by reusing software components. Then, we give an analysis of data collected from the development of an object-oriented software using a GUI framework. We compare the original metrics with the revised ones by evaluating the accuracy of estimating the effort to fix faults and show the validity and usefulness of the revised metrics.     

Empirical validation of object-oriented metrics on open source software for fault prediction

Open source software systems are becoming increasingly important these days. Many companies are investing in open source projects and lots of them are also using such software in their own work. But, because open source software is often developed with a different management style than the industrial ones, the quality and reliability of the code needs to be studied. Hence, the characteristics of the source code of these projects need to be measured to obtain more information about it. This paper describes how we calculated the object-oriented metrics given by Chidamber and Kemerer to illustrate how fault-proneness detection of the source code of the open source Web and e-mail suite called Mozilla can be carried out. We checked the values obtained against the number of bugs found in its bug database - called Bugzilla - using regression and machine learning methods to validate the usefulness of these metrics for fault-proneness prediction. We also compared the metrics of several versions of Mozilla to see how the predicted fault-proneness of the software system changed during its development cycle.     

Empirical analysis of CK metrics for object-oriented design complexity: implications for software defects

To produce high quality object-oriented (OO) applications, a strong emphasis on design aspects, especially during the early phases of software development, is necessary. Design metrics play an important role in helping developers understand design aspects of software and, hence, improve software quality and developer productivity. In this paper, we provide empirical evidence supporting the role of OO design complexity metrics, specifically a subset of the Chidamber and Kemerer (1991, 1994) suite (CK metrics), in determining software defects. Our results, based on industry data from software developed in two popular programming languages used in OO development, indicate that, even after controlling for the size of the software, these metrics are significantly associated with defects. In addition, we find that the effects of these metrics on defects vary across the samples from two programming languages-C++ and Java. We believe that these results have significant implications for designing high-quality software products using the OO approach.     

类设计质量评估方法的研究

详细介绍了C&K度量方法,结合灰色关联分析的相关理论,提出了一种基于C&K度量方法和灰色关联分析的类设计质量评估方法。依据C&K度量阈值及可接受类的定义,可以推导出面向对象程序设计中的最佳类设计标准。将各个类与最佳类设计标准进行灰色关联分析,从而评估类设计质量的优劣。该方法为程序设计人员提供了类设计质量优劣的判定依据,并保证其在软件生命周期的早期阶段及时发现设计质量低劣的类并做相应的处理,避免后期开发的软件产品出现故障,能显著提高软件产品的可靠性和可维护性。     

An Investigation into the Applicability and Validity of Object-Oriented Design Metrics

In this paper we consider empirical evidence in support of a set of object-oriented software metrics. In particular, we look at the object oriented design metrics of Chidamber and Kemerer, and their applicability in different application domains.We briefly describe the metrics, and present our empirical findings, arising from our analysis of systems taken from a number of different application domains. Our investigations have led us to conclude that a subset of the metrics can be of great value to software developers, maintainers and project managers.     

Managerial use of metrics for object-oriented software: anexploratory analysis

With the increasing use of object-oriented methods in new software development, there is a growing need to both document and improve current practice in object-oriented design and development. In response to this need, a number of researchers have developed various metrics for object-oriented systems as proposed aids to the management of these systems. In this research, an analysis of a set of metrics proposed by Chidamber and Kemerer (1994) is performed in order to assess their usefulness for practising managers. First, an informal introduction to the metrics is provided by way of an extended example of their managerial use. Second, exploratory analyses of empirical data relating the metrics to productivity, rework effort and design effort on three commercial object-oriented systems are provided. The empirical results suggest that the metrics provide significant explanatory power for variations in these economic variables, over and above that provided by traditional measures, such as size in lines of code, and after controlling for the effects of individual developers     

A Critical Analysis of Current OO Design Metrics

Chidamber and Kemerer (C&K) outlined some initial proposals for language-independent OO design metrics in 1991. This suite is expanded on by C&K in 1994 and the metrics were tested on systems developed in C++ and Smalltalk™. The six metrics making up the C&K suite can be criticised for a number of reasons. This does not make them bad metrics; on the contrary the C&K work represents one of the most thorough treatments of the subject at the current time. However, the authors explicitly state ...there is no reason to believe that the proposed metrics will be found to be comprehensive, and further work could result in additions, changes and possible deletions from this suite. This analysis will serve to make other researchers and practitioners aware of some of the problems that may arise from using these measures. As a by-product, the axioms of E. Weyuker (1983) come under scrutiny in terms of their applicability to object-oriented metrics. This revised version was published online in August 2006 with corrections to the Cover Date.     

Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes

Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.     

Comments on “a metrics suite for object orienteddesign”

A suite of object oriented software metrics has recently been proposed by S.R. Chidamber and C.F. Kemerer (see ibid., vol. 20, p. 476-94, 1994). While the authors have taken care to ensure their metrics have a sound measurement theoretical basis, we argue that is premature to begin applying such metrics while there remains uncertainty about the precise definitions of many of the quantities to be observed and their impact upon subsequent indirect metrics. In particular, we show some of the ambiguities associated with the seemingly simple concept of the number of methods per class. The usefulness of the proposed metrics, and others, would be greatly enhanced if clearer guidance concerning their application to specific languages were to be provided. Such empirical considerations are as important as the theoretical issues raised by the authors     

Code smells as system-level indicators of maintainability: An empirical study

Context

Code smells are manifestations of design flaws that can degrade code maintainability. So far, no research has investigated if these indicators are useful for conducting system-level maintainability evaluations.

Aim

The research in this paper investigates the potential of code smells to reflect system-level indicators of maintainability.

Method

We evaluated four medium-sized Java systems using code smells and compared the results against previous evaluations on the same systems based on expert judgment and the Chidamber and Kemerer suite of metrics. The systems were maintained over a period of up to 4 weeks. During maintenance, effort (person-hours) and number of defects were measured to validate the different evaluation approaches.

Results

Most code smells are strongly influenced by size; consequently code smells are not good indicators for comparing the maintainability of systems differing greatly in size. Also, from the comparison of the different evaluation approaches, expert judgment was found as the most accurate and flexible since it considered effects due to the system's size and complexity and could adapt to different maintenance scenarios.

Conclusion

Code smell approaches show promise as indicators of the need for maintenance in a way that other purely metric-based approaches lack.     

Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults

In the last decade, empirical studies on object-oriented design metrics have shown some of them to be useful for predicting the fault-proneness of classes in object-oriented software systems. This research did not, however, distinguish among faults according to the severity of impact. It would be valuable to know how object-oriented design metrics and class fault-proneness are related when fault severity is taken into account. In this paper, we use logistic regression and machine learning methods to empirically investigate the usefulness of object-oriented design metrics, specifically, a subset of the Chidamber and Kemerer suite, in predicting fault-proneness when taking fault severity into account. Our results, based on a public domain NASA data set, indicate that 1) most of these design metrics are statistically related to fault-proneness of classes across fault severity, and 2) the prediction capabilities of the investigated metrics greatly depend on the severity of faults. More specifically, these design metrics are able to predict low severity faults in fault-prone classes better than high severity faults in fault-prone classes     

Software fault prediction metrics: A systematic literature review

ContextSoftware metrics may be used in fault prediction models to improve software quality by predicting fault location.ObjectiveThis paper aims to identify software metrics and to assess their applicability in software fault prediction. We investigated the influence of context on metrics’ selection and performance.MethodThis systematic literature review includes 106 papers published between 1991 and 2011. The selected papers are classified according to metrics and context properties.ResultsObject-oriented metrics (49%) were used nearly twice as often compared to traditional source code metrics (27%) or process metrics (24%). Chidamber and Kemerer’s (CK) object-oriented metrics were most frequently used. According to the selected studies there are significant differences between the metrics used in fault prediction performance. Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics. Process metrics seem to be better at predicting post-release faults compared to any static code metrics.ConclusionMore studies should be performed on large industrial software systems to find metrics more relevant for the industry and to answer the question as to which metrics should be used in a given context.     

Empirical validation of object-oriented metrics for predicting fault proneness models

Empirical validation of software metrics used to predict software quality attributes is important to ensure their practical relevance in software organizations. The aim of this work is to find the relation of object-oriented (OO) metrics with fault proneness at different severity levels of faults. For this purpose, different prediction models have been developed using regression and machine learning methods. We evaluate and compare the performance of these methods to find which method performs better at different severity levels of faults and empirically validate OO metrics given by Chidamber and Kemerer. The results of the empirical study are based on public domain NASA data set. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of models predicted using high severity faults is low as compared with the area under the curve of the model predicted with respect to medium and low severity faults. However, the number of faults in the classes correctly classified by predicted models with respect to high severity faults is not low. This study also shows that the performance of machine learning methods is better than logistic regression method with respect to all the severities of faults. Based on the results, it is reasonable to claim that models targeted at different severity levels of faults could help for planning and executing testing by focusing resources on fault-prone parts of the design and code that are likely to cause serious failures.