This paper presents the results of a study conducted at the University of Maryland in which we experimentally investigated the suite of Object-Oriented (OO) design metrics introduced by [Chidamber&Kemerer, 1994]. In order to do this, we assessed these metrics as predictors of fault-prone classes. This study is complementary to [Li&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 experimental results, the advantages and drawbacks of these OO metrics are discussed. Several of Chidamber&Kemerer's OO metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle. We ...

The increasing importance being placed on software measurement has lead to an increased amount of research developing new software measures. Given the importance of object-oriented development techniques, one specific area where this has occurred is coupling measurement in object-oriented systems. However, despite a very interesting and rich body of work, there is little understanding of the motivation and empirical hypotheses behind many of these new measures. It is often difficult to determine how such measures relate to one another and for which application they can be used. As a consequence, it is very difficult for practitioners and researchers to obtain a clear picture of the state-of-the-art in order to select or define measures for object-oriented systems. This situation is addressed and clarified through several different activities. First, a standardized terminology and formalism for expressing measures is provided which ensures that all measures using it are expressed in a f...

The increasing importance being placed on software measurement has lead to an increased amount of research developing new software measures. Given the importance of object-oriented development techniques, one specific area where this has occurred is cohesion measurement in object-oriented systems. However, despite a very interesting body of work, there is little understanding of the motivation and empirical hypotheses behind many of these new measures. It is often difficult to determine how such measures relate to one another and for which application they can be used. As a consequence, it is very difficult for practitioners and researchers to obtain a clear picture of the state-of-the-art in order to select or define cohesion measures for object-oriented systems. This situation is addressed and clarified through several different activities. First, a standardized terminology and formalism for expressing measures is provided which ensures that all measures using it are expressed in a f...

The first goal of this paper is to empirically explore the relationships between existing object-oriented coupling, cohesion, and inheritance measures and the probability of fault detection in system classes during testing. In other words, we wish to better understand the relationship between existing design measurement in OO systems and the quality of the software developed. The second goal is to propose an investigation and analysis strategy to make these kind of studies more repeatable and comparable, a problem which is pervasive in the literature on quality measurement. Results show that many of the measures capture similar dimensions in the data set, thus reflecting the fact that many of them are based on similar principles and hypotheses. However, it is shown that by using a subset of measures, accurate models can be built to predict which classes contain most of the existing faults. When predicting fault-prone classes, the best model shows a percentage of correct clas...

This paper proposes a comprehensive suite of measures to quantify the level of class coupling during the design of object-oriented systems. This suite takes into account the different OO design mechanisms provided by the C++ language (e.g., friendship between classes, specialization, and aggregation) but it can be tailored to other OO languages. The different measures in our suite thus reflect different hypotheses about the different mechanisms of coupling in OO systems. Based on actual project defect data, the hypotheses underlying our coupling measures are empirically validated by analyzing their relationship with the probability of fault detection across classes. The results demonstrate that some of these coupling measures may be useful early quality indicators of the design of OO systems. These measures are conceptually different from the OO design measures defined by Chidamber and Kemerer; in addition, our data suggests that they are complementary quality indicators.

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 [10] is performed in order to assess their usefulness for practicing 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.

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. In this paper, we introduce and compare various high-level design measures for object-based software systems. The measures

Contemporary evidence suggests that most field faults in software applications are found in a smafi percentage of the software's components. This means that if these faulty software components can be detected early in the development project's life cycle, mitigating actions can be taken, such as a redesign. For object-oriented applications, prediction models using design metrics can be used to identify faulty classes early on. In this paper we report on a study that used object-oriented design metrics to construct such prediction models. The study used data collected from one version of a commercial Java application for constructing a prediction model. The model was then validated on a subsequent release of the same application. Our results indicate that the prediction model has a high accuracy. Furthermore, we found that an export coupling metric had the strongest association with faultproneness, indicating a structural feature that may be symptomatic of a class with a high probability of latent faults.

This paper describes the results of a study where the impact of Object-Oriented design on software quality characteristics is experimentally evaluated. A suite of metrics for OO design, called MOOD, was adopted to measure the use of OO design mechanisms. Data collected on the development of eight small-sized information management systems based on identical requirements were used to assess the referred impact. Data obtained in this experiment show how OO design mechanisms such as inheritance, polymorphism, information hiding and coupling, can influence quality characteristics like reliability or maintainability. Some predictive models based on OO design metrics are also presented.

practitioners and their customers who are familiar with the effects of large deviations between planned time for delivery and the actual one. Time is often estimated based on the size of the software to build and it is therefore interesting to investigate how well experienced software developers predict change.