Abstract not found

The benefits that a software organization obtains from estimates of product quality are dependent upon how early in the product cycle that these estimates are available. Early estimation of software quality can help organizations make informed decisions about corrective actions. To provide such early estimates we present an empirical case study of two large scale commercial operating systems, Windows XP and Windows Server 2003. In particular, we leverage various historical in-process and product metrics from Windows XP binaries to create statistical predictors to estimate the post-release failures/failure-proneness of Windows Server 2003 binaries. These models estimate the failures and failure-proneness of Windows Server 2003 binaries at statistically significant levels. Our study is unique in showing that historical predictors for a software product line can be useful, even at the very large scale of the Windows operating system. 1.

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.

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

Abstract not found

This paper describes the issue of software aging and the phenomenon known as code decay. The paper is gives an introduction to the subject, describes the driving forces behind code decay and also presents research on measurement of code decay.