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 mid-size Java systems developed in the same environment. Another contribution of the paper is the use of a novel exploratory analysis technique - MARS (multivariate adaptive regression splines) to build such fault-proneness models, whose functional form is a-priori unknown. The 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.

In the context of software cost estimation, system size is widely taken as a main driver of system development effort. However, other structural design properties, such as coupling, cohesion, and complexity, have been suggested as additional cost factors. Using effort data from an object-oriented development project, we empirically investigate the relationship between class size and the development effort for a class and what additional impact structural properties such as class coupling have on effort. The paper proposes a practical, repeatable, and accurate analysis procedure to investigate relationships between structural properties and development effort. Results indicate that fairly accurate predictions of class effort can be made based on simple measures of the class interface size alone (mean MREs below 30 percent). Effort predictions at the system level are even more accurate as, using Bootstrapping, the estimated 95 percent confidence interval for MREs is 3 to 23 percent. But, more sophisticated coupling and cohesion measures do not help to improve these predictions to a degree that would be practically significant. However, the use of hybrid models combining Poisson regression and CART regression trees clearly improves the accuracy of the models as compared to using Poisson regression alone.

S.345-354 : Ill., Lit.

Many coupling measures have been proposed in the context of object oriented (OO) systems. In addition, due to the numerous dependencies present in OO systems, several studies have highlighted the complexity of using dependency analysis to perform impact analysis. An alternative is to investigate the construction of probabilistic decision models based on coupling measurement to support impact analysis. In addition to providing an ordering of classes where ripple effects are more likely, such an approach is simple and can be automated. In our investigation, we perform a thorough analysis on a commercial C++ system where change data has been collected over several years. We identify the coupling dimensions that seem to be significantly related to ripple effects and use these dimensions to rank classes according to their probability of containing ripple effects. We then assess the expected effectiveness of such decision models.

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 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 fully consistent and operational manner. Second, to provide a structured synthesis, a review of the existing frameworks and measures for coupling measurement 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. This paper contributes to an increased understanding of the state-of-the-art.

The paper aims at empirically exploring the relationships between existing object oriented coupling, cohesion, and inheritance measures and the probability of fault detection in system classes during testing. The underlying goal of such a study is to better understand the relationship between existing product measurement in OO systems and the quality of the software developed. It is shown that by using a subset of existing measures, accurate models can be built to predict in which classes most of the faults are likely to lie in. By inspecting 48% of the classes, it is possible to find 95% of the faults. Besides the size of classes, the frequency of method invocations and the depth of inheritance hierarchies seem to be the main driving factors of fault proneness.