In this paper, we report on an empirical study we have conducted at Ericsson to understand the handling of crash reports (CRs). The study was performed on a dataset of CRs spanning over two years of activities on one of Ericsson's largest systems (+4 Million LOC). CRs at Ericsson are divided into two types: Internal and External. Internal CRs are reported within the organization after the integration and system testing phase. External CRs are submitted by customers and caused mainly by field failures. We examine the proportion and severity of internal CRs and that of external CRs. A large number of external (and severe) CRs could indicate flaws in the testing phase. Failing to react quickly to external CRs, on the other hand, may expose Ericsson to fines and penalties due to the Working Level Agreements (WLA) that Ericsson has with its customers. Moreover, we contrast the time it takes to handle each type of CRs with the dual aim to understand the similarities and differences as well as the factors that impact the handling of each type of CRs. Our results show that (a) it takes more time to fix external CRs compared to internal CRs, (b) the severity attribute is used inconsistently through organizational units, (c) assignment time of internal CRs is less than that of external CRs, (d) More than 50% of CRs are not answered within the organization's fixing time requirements defined in WLA.

Developers may introduce anti-patterns in their software systems because of time pressure, lack of understanding, communication, and--or skills. Anti-patterns impede development and maintenance activities by making the source code more difficult to understand. Detecting anti-patterns in a whole software system may be infeasible because of the required parsing time and of the subsequent needed manual validation. Detecting anti-patterns on subsets of a system could reduce costs, effort, and resources. Researchers have proposed approaches to detect occurrences of anti-patterns but these approaches have currently some limitations: they require extensive knowledge of anti-patterns, they have limited precision and recall, and they cannot be applied on subsets of systems. To overcome these limitations, we introduce SVMDetect, a novel approach to detect anti-patterns, based on a machine learning technique---support vector machines. Indeed, through an empirical study involving three subject systems and four anti-patterns, we showed that the accuracy of SVMDetect is greater than of DETEX when detecting anti-patterns occurrences on a set of classes. Concerning, the whole system, SVMDetect is able to find more anti-patterns occurrences than DETEX.

Social networking sites (SNS) have become very popular during the past few years, as they allow users to both express their individuality and meet people with similar interests. Nonetheless, there are also many potential threats to privacy associated with these SNS such as identity theft and disclosure of sensitive information. However, many users still are not aware of these threats and the privacy settings provided by SNS are not flexible enough to protect user data. In addition, users do not have any control over what others reveal about them. As such, we conduct a preliminary study which examines the privacy protection issues on social networking sites (SNS) such as MySpace, Facebook and LinkedIn. Based on this study, we identify three privacy problems in SNS and propose a privacy framework as a foundation to cope with these problems.