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...

Abstract. The increasing importance being placed on software measurement has led 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 fully consistent and operational manner. Second, to provide a structured synthesis, a review of the existing approaches to measure cohesion in object-oriented systems takes place. Third, a unified framework, based on the issues discovered in the review, is provided and all existing measures are then classified according to this framework. Finally, a review of the empirical validation work concerning existing cohesion measures is provided. This paper contributes to an increased understanding of the state-of-the-art: a mechanism is provided for comparing measures and their potential use, integrating existing measures which examine the same concepts in

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...

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.

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This paper describes the results of an investigation into a set of metrics for object-oriented design, called the MOOD metrics. The merits of each of the six MOOD metrics is discussed from a measurement theory viewpoint, taking into account the recognized object-oriented features which they were intended to measure: encapsulation, inheritance, coupling, and polymorphism. Empirical data, collected from three different application domains, is then analyzed using the MOOD metrics, to support this theoretical validation. Results show that (with appropriate changes to remove existing problematic discontinuities) the metrics could be used to provide an overall assessment of a software system, which may be helpful to managers of software development projects. However, further empirical studies are needed before these results can be generalized.

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

A number of papers have investigated the relationships between design metrics and the detection of faults in objectoriented software. Several of these studies have shown that such models can be accurate in predicting faulty classes within one particular software product. In practice, however, prediction models are built on certain products to be used on subsequent software development projects. How accurate can these models be considering the inevitable differences that may exist across projects and systems? Organizations typically learn and change. From a more general standpoint, can we obtain any evidence that such models are economically viable tools to focus validation and verification effort? This paper attempts to answer these questions using fault and design data collected on two midsize Java systems developed in the same environment. Another contribution of the paper is the use of a novel exploratory analysis technique (MARS) to build such faultproneness models, whose functional form is a priori unknown. Results indicate that a model built on one system can be accurately used to rank classes within another system according to their fault-proneness. The downside, however, is that, because of system differences, the predicted fault probabilities are not representative of the system predicted. Furthermore, a cost-benefit model demonstrates that the MARS fault-proneness model is potentially viable, from an economical standpoint. The linear model is not, thus suggesting a more complex model is required.

We propose an approach (GQM/MEDEA) for defining measures of product attributes in software engineering. The approach is driven by the experimental goals of measurement, expressed via the GQM paradigm, and a set of empirical hypotheses. To make

Despite significant progress in the last 15 years, implementing a successful measurement program for software development is still a challenging undertaking. Most problems are not of theoretical but of methodological or practical nature. In this article, we present lessons learned from experiences with goal-oriented measurement. We structure them into practical guidelines for efficient and useful software measurement aimed at process improvement in industry. Issues related to setting measurement goals, defining explicit measurement models, and implementing data collection procedures are addressed from a practical perspective. In addition, guidelines for using measurement in the context of process improvement are provided.