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 also showed that they are, on our data set, better predictors than "traditional" code metrics, which can only be collected at a later phase of the software development processes.

Abstract

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.

A number of papers have investigated the relationships between design metrics and the detection of faults in object-oriented 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 by devising a general but tailorable cost-benefit model and by 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 fault-proneness 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, However, our cost-benefit model demonstrates that the MARS fault-proneness model is potentially viable, from an economical standpoint, The linear model is not nearly as good, thus suggesting a more complex model is required

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