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     

An empirical analysis of package-modularization metrics: Implications for software fault-proneness

ContextIn a large object-oriented software system, packages play the role of modules which group related classes together to provide well-identified services to the rest of the system. In this context, it is widely believed that modularization has a large influence on the quality of packages. Recently, Sarkar, Kak, and Rama proposed a set of new metrics to characterize the modularization quality of packages from important perspectives such as inter-module call traffic, state access violations, fragile base-class design, programming to interface, and plugin pollution. These package-modularization metrics are quite different from traditional package-level metrics, which measure software quality mainly from size, extensibility, responsibility, independence, abstractness, and instability perspectives. As such, it is expected that these package-modularization metrics should be useful predictors for fault-proneness. However, little is currently known on their actual usefulness for fault-proneness prediction, especially compared with traditional package-level metrics.ObjectiveIn this paper, we examine the role of these new package-modularization metrics for determining software fault-proneness in object-oriented systems.MethodWe first use principal component analysis to analyze whether these new package-modularization metrics capture additional information compared with traditional package-level metrics. Second, we employ univariate prediction models to investigate how these new package-modularization metrics are related to fault-proneness. Finally, we build multivariate prediction models to examine the ability of these new package-modularization metrics for predicting fault-prone packages.ResultsOur results, based on six open-source object-oriented software systems, show that: (1) these new package-modularization metrics provide new and complementary views of software complexity compared with traditional package-level metrics; (2) most of these new package-modularization metrics have a significant association with fault-proneness in an expected direction; and (3) these new package-modularization metrics can substantially improve the effectiveness of fault-proneness prediction when used with traditional package-level metrics together.ConclusionsThe package-modularization metrics proposed by Sarkar, Kak, and Rama are useful for practitioners to develop quality software systems.     

Empirical analysis of network measures for predicting high severity software faults

Network measures are useful for predicting fault-prone modules. However, existing work has not distinguished faults according to their severity. In practice, high severity faults cause serious problems and require further attention. In this study, we explored the utility of network measures in high severity faultproneness prediction. We constructed software source code networks for four open-source projects by extracting the dependencies between modules. We then used univariate logistic regression to investigate the associations between each network measure and fault-proneness at a high severity level. We built multivariate prediction models to examine their explanatory ability for fault-proneness, as well as evaluated their predictive effectiveness compared to code metrics under forward-release and cross-project predictions. The results revealed the following: (1) most network measures are significantly related to high severity fault-proneness; (2) network measures generally have comparable explanatory abilities and predictive powers to those of code metrics; and (3) network measures are very unstable for cross-project predictions. These results indicate that network measures are of practical value in high severity fault-proneness prediction.     

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.     

Deriving thresholds of software metrics to predict faults on open source software: Replicated case studies

Object-oriented metrics aim to exhibit the quality of source code and give insight to it quantitatively. Each metric assesses the code from a different aspect. There is a relationship between the quality level and the risk level of source code. The objective of this paper is to empirically examine whether or not there are effective threshold values for source code metrics. It is targeted to derive generalized thresholds that can be used in different software systems. The relationship between metric thresholds and fault-proneness was investigated empirically in this study by using ten open-source software systems. Three types of fault-proneness were defined for the software modules: non-fault-prone, more-than-one-fault-prone, and more-than-three-fault-prone. Two independent case studies were carried out to derive two different threshold values. A single set was created by merging ten datasets and was used as training data by the model. The learner model was created using logistic regression and the Bender method. Results revealed that some metrics have threshold effects. Seven metrics gave satisfactory results in the first case study. In the second case study, eleven metrics gave satisfactory results. This study makes contributions primarily for use by software developers and testers. Software developers can see classes or modules that require revising; this, consequently, contributes to an increment in quality for these modules and a decrement in their risk level. Testers can identify modules that need more testing effort and can prioritize modules according to their risk levels.     

On the ability of complexity metrics to predict fault-prone classes in object-oriented systems

Many studies use logistic regression models to investigate the ability of complexity metrics to predict fault-prone classes. However, it is not uncommon to see the inappropriate use of performance indictors such as odds ratio in previous studies. In particular, a recent study by Olague et al. uses the odds ratio associated with one unit increase in a metric to compare the relative magnitude of the associations between individual metrics and fault-proneness. In addition, the percents of concordant, discordant, and tied pairs are used to evaluate the predictive effectiveness of a univariate logistic regression model. Their results suggest that lesser known complexity metrics such as standard deviation method complexity (SDMC) and average method complexity (AMC) are better predictors than the two commonly used metrics: lines of code (LOC) and weighted method McCabe complexity (WMC). In this paper, however, we show that (1) the odds ratio associated with one standard deviation increase, rather than one unit increase, in a metric should be used to compare the relative magnitudes of the effects of individual metrics on fault-proneness. Otherwise, misleading results may be obtained; and that (2) the connection of the percents of concordant, discordant, and tied pairs with the predictive effectiveness of a univariate logistic regression model is false, as they indeed do not depend on the model. Furthermore, we use the data collected from three versions of Eclipse to re-examine the ability of complexity metrics to predict fault-proneness. Our experimental results reveal that: (1) many metrics exhibit moderate or almost moderate ability in discriminating between fault-prone and not fault-prone classes; (2) LOC and WMC are indeed better fault-proneness predictors than SDMC and AMC; and (3) the explanatory power of other complexity metrics in addition to LOC is limited.     

一种基于切片技术度量Java耦合性的框架

在研究面向对象的度量问题时,人们通过简单的统计方法和基于信息源的方法来度量其中的一些特征,例如基本度量、CK度量和AoKi度量等。文中采用一种基于程序切片的方法来度量Java的耦合性问题,通过对J ava源程序中存在的耦合关系的度量,得到了一种比传统方法更精确的耦合度量方法。     

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     

Empirical analysis of network measures for effort-aware fault-proneness prediction

ContextRecently, network measures have been proposed to predict fault-prone modules. Leveraging the dependency relationships between software entities, network measures describe the structural features of software systems. However, there is no consensus about their effectiveness for fault-proneness prediction. Specifically, the predictive ability of network measures in effort-aware context has not been addressed.ObjectiveWe aim to provide a comprehensive evaluation on the predictive effectiveness of network measures with the effort needed to inspect the code taken into consideration.MethodWe first constructed software source code networks of 11 open-source projects by extracting the data and call dependencies between modules. We then employed univariate logistic regression to investigate how each single network measure was correlated with fault-proneness. Finally, we built multivariate prediction models to examine the usefulness of network measures under three prediction settings: cross-validation, across-release, and inter-project predictions. In particular, we used the effort-aware performance indicators to compare their predictive ability against the commonly used code metrics in both ranking and classification scenarios.ResultsBased on the 11 open-source software systems, our results show that: (1) most network measures are significantly positively related to fault-proneness; (2) the performance of network measures varies under different prediction settings; (3) network measures have inconsistent effects on various projects.ConclusionNetwork measures are of practical value in the context of effort-aware fault-proneness prediction, but researchers and practitioners should be careful of choosing whether and when to use network measures in practice.     

Dynamic coupling measurement for object-oriented software

The relationships between coupling and external quality factors of object-oriented software have been studied extensively for the past few years. For example, several studies have identified clear empirical relationships between class-level coupling and class fault-proneness. 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 ("dead") code in commercial software, the resulting coupling measures are imprecise as they do not perfectly reflect the actual coupling taking place among classes at runtime. 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. Coupling measurement has traditionally been performed using static code analysis, because most of the existing work was done on nonobject oriented code and because dynamic code analysis is more expensive and complex to perform. For modern software systems, however, this focus on static analysis can be problematic because although dynamic binding existed before the advent of object-orientation, its usage has increased significantly in the last decade. We describe how coupling can be defined and precisely measured based on dynamic analysis of systems. We refer to this type of coupling as dynamic coupling. An empirical evaluation of the proposed dynamic coupling measures is reported in which we study the relationship of these measures with the change proneness of classes. Data from maintenance releases of a large Java system are used for this purpose. Preliminary results suggest that some dynamic coupling measures are significant indicators of change proneness and that they complement existing coupling measures based on static analysis.     

Applying machine learning to software fault-proneness prediction

The importance of software testing to quality assurance cannot be overemphasized. The estimation of a module’s fault-proneness is important for minimizing cost and improving the effectiveness of the software testing process. Unfortunately, no general technique for estimating software fault-proneness is available. The observed correlation between some software metrics and fault-proneness has resulted in a variety of predictive models based on multiple metrics. Much work has concentrated on how to select the software metrics that are most likely to indicate fault-proneness. In this paper, we propose the use of machine learning for this purpose. Specifically, given historical data on software metric values and number of reported errors, an Artificial Neural Network (ANN) is trained. Then, in order to determine the importance of each software metric in predicting fault-proneness, a sensitivity analysis is performed on the trained ANN. The software metrics that are deemed to be the most critical are then used as the basis of an ANN-based predictive model of a continuous measure of fault-proneness. We also view fault-proneness prediction as a binary classification task (i.e., a module can either contain errors or be error-free) and use Support Vector Machines (SVM) as a state-of-the-art classification method. We perform a comparative experimental study of the effectiveness of ANNs and SVMs on a data set obtained from NASA’s Metrics Data Program data repository.     

Defining and validating measures for object-based high-level design

The availability of significant measures in the early phases of the software development life-cycle allows for better management of the later phases, and more effective quality assessment when quality can be more easily affected by preventive or corrective actions. We introduce and compare various high-level design measures for object-based software systems. The measures are derived based on an experimental goal, identifying fault-prone software parts, and several experimental hypotheses arising from the development of Ada systems for Flight Dynamics Software at the NASA Goddard Space Flight Center (NASA/GSFC). Specifically, we define a set of measures for cohesion and coupling, which satisfy a previously published set of mathematical properties that are necessary for any such measures to be valid. We then investigate the measures' relationship to fault-proneness on three large scale projects, to provide empirical support for their practical significance and usefulness     

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.     

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.     

Package Coupling Measurement in Object-Oriented Software

The grouping of correlated classes into a package helps in better organization of modern object-oriented software. The quality of such packages needs to be measured so as to estimate their utilization. In this paper, new package coupling metrics are proposed, which also take into consideration the hierarchical structure of packages and direction of connections among package elements. The proposed measures have been validated theoretically as well as empirically using 18 packages taken from two open source software systems. The results obtained from this study show strong correlation between package coupling and understandability of the package which suggests that proposed metrics could be further used to represent other external software quality factors.     

Coupling metrics for ontology-based system

Measuring system coupling is a commonly accepted software engineering practice associated with producing high-quality software products. Coupling metrics traditionally measure data passed across a module interface to determine couplings between modules in a given system. XML has become common in Internet-based application domains such as business-to-business and business-to-consumer applications, and has formed a basis for service-oriented architectures such as Web services and the Semantic Web. We therefore need new coupling metrics that address these systems' unique requirements. We propose a set of coupling metrics for ontology-based systems represented in OWL: the number of external classes (NEC), reference to external classes (REC), and referenced includes (RI). To collect these metrics, we use a standard XML-based parser. This research reflects a new type of coupling measurement for system development that defines coupling metrics based on ontology data and its structure.     

Using information retrieval based coupling measures for impact analysis

Coupling is an important property of software systems, which directly impacts program comprehension. In addition, the strength of coupling measured between modules in software is often used as a predictor of external software quality attributes such as changeability, ripple effects of changes and fault-proneness. This paper presents a new set of coupling measures for Object-Oriented (OO) software systems measuring conceptual coupling of classes. Conceptual coupling is based on measuring the degree to which the identifiers and comments from different classes relate to each other. This type of relationship, called conceptual coupling, is measured through the use of Information Retrieval (IR) techniques. The proposed measures are different from existing coupling measures and they capture new dimensions of coupling, which are not captured by the existing coupling measures. The paper investigates the use of the conceptual coupling measures during change impact analysis. The paper reports the findings of a case study in the source code of the Mozilla web browser, where the conceptual coupling metrics were compared to nine existing structural coupling metrics and proved to be better predictors for classes impacted by changes.

Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization

The identification of a module's fault-proneness is very important for minimizing cost and improving the effectiveness of the software development process. How to obtain the correlation between software metrics and module's fault-proneness has been the focus of much research. This paper presents the application of hybrid artificial neural network (ANN) and Quantum Particle Swarm Optimization (QPSO) in software fault-proneness prediction. ANN is used for classifying software modules into fault-proneness or non fault-proneness categories, and QPSO is applied for reducing dimensionality. The experiment results show that the proposed prediction approach can establish the correlation between software metrics and modules’ fault-proneness, and is very simple because its implementation requires neither extra cost nor expert's knowledge. Proposed prediction approach can provide the potential software modules with fault-proneness to software developers, so developers only need to focus on these software modules, which may minimize effort and cost of software maintenance.     

Evaluating the impact of design pattern and anti-pattern dependencies on changes and faults

On the one hand, design patterns are solutions to recurring design problems, aimed at increasing reuse, flexibility, and maintainability. However, much prior work found that some patterns, such as the Observer and Singleton, are correlated with large code structures and argued that they are more likely to be fault prone. On the other hand, anti-patterns describe poor solutions to design and implementation problems that highlight weaknesses in the design of software systems and that may slow down maintenance and increase the risk of faults. They have been found to negatively impact change and fault-proneness. Classes participating in design patterns and anti-patterns have dependencies with other classes, e.g., static and co-change dependencies, that may propagate problems to other classes. We investigate the impact of such dependencies in object-oriented systems by studying the relations between the presence of static and co-change dependencies and (1) the fault-proneness, (2) the types of changes, and (3) the types of faults that these classes exhibit. We analyze six design patterns and 10 anti-patterns in 39 releases of ArgoUML, JFreeChart, and XercesJ, and investigate to what extent classes having dependencies with design patterns or anti-patterns have higher odds of faults than other classes. We show that in almost all releases of the three systems, classes having dependencies with anti-patterns are more fault-prone than others while this is not always true for classes with dependencies with design patterns. We also observe that structural changes are the most common changes impacting classes having dependencies with anti-patterns. Software developers could use this knowledge about the impact of design pattern and anti-pattern dependencies to better focus their testing and reviewing activities towards the most risky classes and to propagate changes adequately.     

Quantifying structural attributes of system decompositions in 28 feature-oriented software product lines

A key idea of feature orientation is to decompose a software product line along the features it provides. Feature decomposition is orthogonal to object-oriented decomposition—it crosscuts the underlying package and class structure. It has been argued often that feature decomposition improves system structure by reducing coupling and by increasing cohesion. However, recent empirical findings suggest that this is not necessarily the case. In this exploratory, observational study, we investigate the decompositions of 28 feature-oriented software product lines into classes, features, and feature-specific class fragments. The product lines under investigation are implemented using the feature-oriented programming language Fuji. In particular, we quantify and compare the internal attributes import coupling and cohesion of the different product-line decompositions in a systematic, reproducible manner. For this purpose, we adopt three established software measures (e.g., coupling between units, CBU; internal-ratio unit dependency, IUD) as well as standard concentration statistics (e.g., Gini coefficient). In our study, we found that feature decomposition can be associated with higher levels of structural coupling in a product line than a decomposition into classes. Although coupling can be concentrated in very few features in most feature decompositions, there are not necessarily hot-spot features in all product lines. Interestingly, feature cohesion is not necessarily higher than class cohesion, whereas features are more equal in serving dependencies internally than classes of a product line. Our empirical study raises critical questions about alleged advantages of feature decomposition. At the same time, we demonstrate how our measurement approach of coupling and cohesion has potential to support static and dynamic analyses of software product lines (i.e., type checking and feature-interaction detection) by facilitating product sampling.