Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness

Previous research shows that class size can influence the associations between object-oriented (OO) metrics and fault-proneness and therefore proposes that it should be controlled as a confounding variable when validating OO metrics on fault-proneness. Otherwise, their true associations may be distorted. However, it has not been determined whether this practice is equally applicable to other external quality attributes. In this paper, we use three size metrics, two of which are available during the high-level design phase, to examine the potentially confounding effect of class size on the associations between OO metrics and change-proneness. The OO metrics that are investigated include cohesion, coupling, and inheritance metrics. Our results, based on Eclipse, indicate that: 1) The confounding effect of class size on the associations between OO metrics and change-proneness, in general, exists, regardless of whichever size metric is used; 2) the confounding effect of class size generally leads to an overestimate of the associations between OO metrics and change-proneness; and 3) for many OO metrics, the confounding effect of class size completely accounts for their associations with change-proneness or results in a change of the direction of the associations. These results strongly suggest that studies validating OO metrics on change-proneness should also consider class size as a confounding variable.     

Improving the applicability of object-oriented class cohesion metrics

Context

Class cohesion is an important object-oriented quality attribute. It refers to the degree of relatedness between the methods and attributes of a class. Several metrics have been proposed to measure the extent to which the class members are related. Most of these metrics have undefined values for a relatively high percentage of classes, which limits their applicability. The classes that have undefined values lack methods, attributes, or parameter types, or they include only a single method.

Objective

We improve the applicability of the class cohesion metrics by defining their values for such special classes. In addition, we theoretically and empirically validate the improved metrics.

Method

We theoretically examine whether the defined values satisfy the key cohesion properties. In addition, we empirically validate the metrics before and after the improvements to test whether the defined values improve the ability of the metrics to evaluate class cohesion. We also explore the correlation between the metrics and the presence of faulty classes to indirectly determine the strength or weakness of the metrics in indicating class quality.

Results

The results show that our assigned values for the undefined cases do not violate the key cohesion properties and considerably improve the ability of the metrics to explain the presence of faulty classes and may therefore improve their ability to indicate the quality of the class design.

Conclusions

Having the class cohesion metrics defined for all possible cases improves the applicability of the metrics and potentially increases their precision in indicating class quality.     

The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process

Many empirical studies have found that software metrics can predict class error proneness and the prediction can be used to accurately group error-prone classes. Recent empirical studies have used open source systems. These studies, however, focused on the relationship between software metrics and class error proneness during the development phase of software projects. Whether software metrics can still predict class error proneness in a system’s post-release evolution is still a question to be answered. This study examined three releases of the Eclipse project and found that although some metrics can still predict class error proneness in three error-severity categories, the accuracy of the prediction decreased from release to release. Furthermore, we found that the prediction cannot be used to build a metrics model to identify error-prone classes with acceptable accuracy. These findings suggest that as a system evolves, the use of some commonly used metrics to identify which classes are more prone to errors becomes increasingly difficult and we should seek alternative methods (to the metric-prediction models) to locate error-prone classes if we want high accuracy.     

Fault prediction and the discriminative powers of connectivity-based object-oriented class cohesion metrics

Context

Several metrics have been proposed to measure the extent to which class members are related. Connectivity-based class cohesion metrics measure the degree of connectivity among the class members.

Objective

We propose a new class cohesion metric that has higher discriminative power than any of the existing cohesion metrics. In addition, we empirically compare the connectivity and non-connectivity-based cohesion metrics.

Method

The proposed class cohesion metric is based on counting the number of possible paths in a graph that represents the connectivity pattern of the class members. We theoretically and empirically validate this path connectivity class cohesion (PCCC) metric. The empirical validation compares seven connectivity-based metrics, including PCCC, and 11 non-connectivity-based metrics in terms of discriminative and fault detection powers. The discriminative-power study explores the probability that a cohesion metric will incorrectly determine classes to be cohesively equal when they have different connectivity patterns. The fault detection study investigates whether connectivity-based metrics, including PCCC, better explain the presence of faults from a statistical standpoint in comparison to other non-connectivity-based cohesion metrics, considered individually or in combination.

Results

The theoretical validation demonstrates that PCCC satisfies the key cohesion properties. The results of the empirical studies indicate that, in contrast to other connectivity-based cohesion metrics, PCCC is much better than any comparable cohesion metric in terms of its discriminative power. In addition, the results also indicate that PCCC measures cohesion aspects that are not captured by other metrics, wherein it is considerably better than other connectivity-based metrics but slightly worse than some other non-connectivity-based cohesion metrics in terms of its ability to predict faulty classes.

Conclusion

PCCC is more useful in practice for the applications in which practitioners need to distinguish between the quality of different classes or the quality of different implementations of the same class.     

An Empirical Study of Software Metrics

Software metrics are computed for the purpose of evaluating certain characteristics of the software developed. A Fortran static source code analyzer, FORTRANAL, was developed to study 31 metrics, including a new hybrid metric introduced in this paper, and applied to a database of 255 programs, all of which were student assignments. Comparisons among these metrics are performed. Their cross-correlation confirms the internal consistency of some of these metrics which belong to the same class. To remedy the incompleteness of most of these metrics, the proposed metric incorporates context sensitivity to structural attributes extracted from a flow graph. It is also concluded that many volume metrics have similar performance while some control metrics surprisingly correlate well with typical volume metrics in the test samples used. A flexible class of hybrid metric can incorporate both volume and control attributes in assessing software complexity.     

面向对象程序复杂性度量层次模型

程序复杂性度量可以实现定量地分析程序复杂性，从而为估计成本提供一个标准。为了度量面向对象程序复杂性，在讨论了度量的定义．度量的理论基础后，提出了一种面向对象程序复杂性度量的层次模型。该模型分成5个层次：系统层、类簇层、类继承树层、类层和方法层，每个层次都有自己的度量方法。采用这种分层度量模型的好处是：它是一个框架，各度量方法归属到各层中；各层之间相互独立；一层中方法的修改并不影响其它层。     

Channel sharing by multi-class rate adaptive streams: Performance region and optimization

We consider the problem of channel sharing by rate adaptive streams belonging to various classes. The performance metric per class is the mean bandwidth allocated to connections in the class possibly scaled by appropriate factors. We first provide a bandwidth adaptation policy that maximizes a linear combination of class performance metrics; then, we use this result to characterize the region where the class performance metrics lie under any bandwidth adaptation policy. Based on the results above we use stochastic approximation techniques to provide a policy that optimizes a combination of concave rewards associated with class performance metrics. Finally, we propose a modification of the optimal policy to account for the case where connection holding times are unknown, and study its performance through simulations.     

面向对象软件中类内聚度度量分析与研究*

对现有著名的类内聚度度量进行了较深入的分析,指出了各自存在的缺陷与不足,并在此基础上考虑到类内聚度受到类内有可能与外界组件产生耦合的成员的负面影响,提出了对CCM(C)再次修正后的MCCM2(C).     

Client-based cohesion metrics for Java programs

One purpose of software metrics is to measure the quality of programs. The results can be for example used to predict maintenance costs or improve code quality. An emerging view is that if software metrics are going to be used to improve quality, they must help in finding code that should be refactored. Often refactoring or applying a design pattern is related to the role of the class to be refactored. In client-based metrics, a project gives the class a context. These metrics measure how a class is used by other classes in the context. We present a new client-based metric LCIC (Lack of Coherence in Clients), which analyses if the class being measured has a coherent set of roles in the program. Interfaces represent the roles of classes. If a class does not have a coherent set of roles, it should be refactored, or a new interface should be defined for the class.We have implemented a tool for measuring the metric LCIC for Java projects in the Eclipse environment. We calculated LCIC values for classes of several open source projects. We compare these results with results of other related metrics, and inspect the measured classes to find out what kind of refactorings are needed. We also analyse the relation of different design patterns and refactorings to our metric. Our experiments reveal the usefulness of client-based metrics to improve the quality of code.     

Spectral‐based fault localization using hyperbolic function

Debugging is crucial for producing reliable software. One of the effective bug localization techniques is spectral‐based fault localization. It tries to locate a buggy statement by applying an evaluation metric to program spectra and ranking program components on the basis of the score it computes. Here, we propose a restricted class of “hyperbolic” metrics, with a small number of numeric parameters. This class of functions is based on past theoretical and empirical results. We show that optimization methods such as genetic programming and simulated annealing can reliably discover effective metrics over a wide range of data sets of program spectra. We evaluate the performance for both real programs and model programs with single bugs, multiple bugs, “deterministic” bugs, and nondeterministic bugs and find that the proposed class of metrics performs as well as or better than the previous best‐performing metrics over a broad range of data.     

Automated measurement of models of requirements

On way to formalize system requirements is to express them using the object-oriented paradigm. In this case, the class model representing the structure of requirements is called a requirements metamodel, and requirements themselves are object-based models of natural language requirements. In this paper, we show that such object-oriented requirements are well suited to support a large class of requirements metrics. We define a requirements metamodel and use an automated measurement approach proposed in our previous work to specify requirements metrics. We show that it is possible to integrate 78 metrics from 11 different papers in the proposed framework. The software that computes the requirements metric values is fully generated from the specification of metrics.     

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.     

The impact of accounting for special methods in the measurement of object-oriented class cohesion on refactoring and fault prediction activities

Class cohesion is a key attribute that is used to assess the design quality of a class, and it refers to the extent to which the attributes and methods of the class are related. Typically, classes contain special types of methods, such as constructors, destructors, and access methods. Each of these special methods has its own characteristics, which can artificially affect the class cohesion measurement. Several metrics have been proposed in the literature to indicate class cohesion during high- or low-level design phases. The impact of accounting for special methods in cohesion measurement has not been addressed for most of these metrics. This paper empirically explores the impact of including or excluding special methods on cohesion measurements that were performed using 20 existing class cohesion metrics. The empirical study applies the metrics that were considered to five open-source systems under four different scenarios, including (1) considering all special methods, (2) ignoring only constructors, (3) ignoring only access methods, and (4) ignoring all special methods. This study empirically explores the impact of including special methods in cohesion measurement for two applications of interest to software practitioners, including refactoring and predicting faulty classes. The results of the empirical studies show that the cohesion values for most of the metrics considered differ significantly across the four scenarios and that this difference significantly affects the refactoring decisions, but does not significantly affect the abilities of the metrics to predict faulty classes.     

面向对象软件内聚度度量数据分布的实证研究

度量数据的分布信息对于理解和使用面向对象软件度量有重要意义.人们对面向对象软件规模度量、耦合度度量乃至继承维度的度量数据的分布都有研究,但对除内聚度缺乏度LCOM之外的内聚度度量数据的分布却缺乏研究.已有的实证研究表明,LCOM并不是好的内聚度度量,因此探讨其他内聚度度量数据分布很有必要.对包括内聚度缺乏度、基于连通性的内聚度度量和基于相似性的内聚度度量总共17个度量指标在112个Java开源软件项目的分布情况进行实证研究,对每个度量指标的每个项目数据使用幂律分布和对数正态分布进行拟合,并使用荟萃分析方法对拟合结果进行了分析.实证研究结果表明,非规范化的内聚度量可使用对数正态分布和幂律分布拟合,但规范化的基于相似性的内聚度量（包括CC、LSCC、SCOM和SCC）需要排除方法数小于等于1或字段数为0的特殊类才能使用对数正态分布拟合,而基于连通性的内聚度度量（包括TCC、LCC、DCD和DCI）则只有对应的非规范化版本的数据才符合对数正态分布或幂律分布.实证研究可帮助人们更好地理解和使用内聚度度量,特别是可以帮助人们如何利用已有的方法确定内聚度度量的阈值.     

Measuring protocol strength with security goals

Flaws in published standards for security protocols are found regularly, often after systems implementing those standards have been deployed. Because of deployment constraints and disagreements among stakeholders, different fixes may be proposed and debated. In this process, security improvements must be balanced with issues of functionality and compatibility. This paper provides a family of rigorous metrics for protocol security improvements. These metrics are sets of first-order formulas in a goal language \(\mathcal {GL}(\varPi )\) associated with a protocol \(\varPi \). The semantics of \(\mathcal {GL}(\varPi )\) is compatible with many ways to analyze protocols, and some metrics in this family are supported by many protocol analysis tools. Other metrics are supported by our Cryptographic Protocol Shapes Analyzer cpsa. This family of metrics refines several “hierarchies” of security goals in the literature. Our metrics are applicable even when, to mitigate a flaw, participants must enforce policies that constrain protocol execution. We recommend that protocols submitted to standards groups characterize their goals using formulas in \(\mathcal {GL}(\varPi )\), and that discussions comparing alternative protocol refinements measure their security in these terms.     

一种UML类图的复杂性和可复用性度量方法

UML类图描述了系统中的类及其相互之间的各种静态关系，为了对UML类图提供客观的评价，通过对典型度量方法的改进，文章采用一组度量从不同侧面来评价一个类图的复杂性和可复用性，并考虑了类图中属性和方法在公有、私有和受保护性质方面的区别。     

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     

The ability of object-oriented metrics to predict change-proneness: a meta-analysis

Many studies have investigated the relationships between object-oriented (OO) metrics and change-proneness and conclude that OO metrics are able to predict the extent of change of a class across the versions of a system. However, there is a need to re-examine this subject for two reasons. First, most studies only analyze a small number of OO metrics and, therefore, it is not clear whether this conclusion is applicable to most, if not all, OO metrics. Second, most studies only uses relatively few systems to investigate the relationships between OO metrics and change-proneness and, therefore, it is not clear whether this conclusion can be generalized to other systems. In this paper, based on 102 Java systems, we employ statistical meta-analysis techniques to investigate the ability of 62 OO metrics to predict change-proneness. In our context, a class which is changed in the next version of a system is called change-prone and not change-prone otherwise. The investigated OO metrics cover four metric dimensions, including 7 size metrics, 18 cohesion metrics, 20 coupling metrics, and 17 inheritance metrics. We use AUC (the area under a relative operating characteristic, ROC) to evaluate the predictive effectiveness of OO metrics. For each OO metric, we first compute AUCs and the corresponding variances for individual systems. Then, we employ a random-effect model to compute the average AUC over all systems. Finally, we perform a sensitivity analysis to investigate whether the AUC result from the random-effect model is robust to the data selection bias in this study. Our results from random-effect models reveal that: (1) size metrics exhibit moderate or almost moderate ability in discriminating between change-prone and not change-prone classes; (2) coupling and cohesion metrics generally have a lower predictive ability compared to size metrics; and (3) inheritance metrics have a poor ability to discriminate between change-prone and not change-prone classes. Our results from sensitivity analyses show that these conclusions reached are not substantially influenced by the data selection bias.     

基于类之间的依赖关系确定类的规模

类之间的依赖关系，对于面向对象系统分析、设计和测试都有重要的意义。本文首先对类之间的依赖关系进行了定义和说明，并细分其为数据依赖和方法依赖，在此基础上，对类之间的依赖关系进行了度量，提出依赖度和被依赖度两种度量方法，并以此确定类地规模大小。     

Improving cohesion metrics for classes by considering dependent instance variables

The existing cohesion metrics for classes do not consider the characteristics of dependent instance variables that are commonly used in a class and, thus, do not properly reflect the cohesiveness of the class. This paper presents an approach for improving the cohesion metrics by considering the characteristics of the dependent instance variables in an object-oriented program.