Commercial software development is a complex task that requires a thorough understanding of the architecture of the software system. We analyze the Windows Server 2003 operating system in order to assess the relationship between its software dependences, churn metrics and post-release failures. Our analysis indicates the ability of software dependences and churn metrics to be efficient predictors of post-release failures. Further, we investigate the relationship between the software dependences and churn metrics and their ability to assess failure-proneness probabilities at statistically significant levels.

In any software project, developers need to be aware of existing dependencies and how they affect their system. We investigated the architecture and dependencies of Windows Server 2003 to show how to use the complexity of a subsystem’s dependency graph to predict the number of failures at statistically significant levels. Such estimations can help to allocate software quality resources to the parts of a product that need it most, and as early as possible. 1.

Prediction of software defects works well within projects as long as there is a sufficient amount of data available to train any models. However, this is rarely the case for new software projects and for many companies. So far, only a few have studies focused on transferring prediction models from one project to another. In this paper, we study cross-project defect prediction models on a large scale. For 12 real-world applications, we ran 622 cross-project predictions. Our results indicate that cross-project prediction is a serious challenge, i.e., simply using models from projects in the same domain or with the same process does not lead to accurate predictions. To help software engineers choose models wisely, we identified factors that do influence the success of cross-project predictions. We also derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted. Categories and Subject Descriptors. D.2.8 [Software Engineering]: Metrics—Performance measures, Process metrics, Product metrics. D.2.9 [Software Engineering]: Management—Software

Often software systems are developed by organizations consisting of many teams of individuals working together. Brooks states in the Mythical Man Month book that product quality is strongly affected by organization structure. Unfortunately there has been little empirical evidence to date to substantiate this assertion. In this paper we present a metric scheme to quantify organizational complexity, in relation to the product development process to identify if the metrics impact failure-proneness. In our case study, the organizational metrics when applied to data from Windows Vista were statistically significant predictors of failure-proneness. The precision and recall measures for identifying failure-prone binaries, using the organizational metrics, was significantly higher than using traditional metrics like churn, complexity, coverage, dependencies, and pre-release bug measures that have been used to date to predict failure-proneness. Our results provide empirical evidence that the organizational metrics are related to, and are effective predictors of failure-proneness.

Often software systems are developed by organizations consisting of many teams of individuals working together. Brooks states in the Mythical Man Month book that product quality is strongly affected by organization structure. Unfortunately there has been little empirical evidence to date to substantiate this assertion. In this paper we present a metric scheme to quantify organizational complexity, in relation to the product development process to identify if the metrics impact failure-proneness. In our case study, the organizational metrics when applied to data from Windows Vista were statistically significant predictors of failure-proneness. The precision and recall measures for identifying failure-prone binaries, using the organizational metrics, was significantly higher than using traditional metrics like churn, complexity, coverage, dependencies, and pre-release bug measures that have been used to date to predict failure-proneness. Our results provide empirical evidence that the organizational metrics are related to, and are effective predictors of failure-proneness.

✦ Core issues in common approaches