Many organisations create, store, or purchase information that links individuals’ identities to other data. Termed personally identifiable information (PII), this information has become the lifeblood of many firms across the globe. As organisations accumulate their constituencies’ PII (e.g. customers’, students’, patients’, and employees’ data), individuals’ privacy will depend on the adequacy of organisations’ information privacy safeguards. Despite existing protections, many breaches still occur. For example, US organisations reported around 4,500 PII-breach events between 2005 and 2015. With such a high number of breaches, determining all threats to PII within organisations proves a burdensome task. In light of this difficulty, we utilise text-mining and cluster analysis techniques to create a taxonomy of various organisational PII breaches, which will help drive targeted research towards organisational PII protection. From an organisational systematics perspective, our classification system provides a foundation to explain the diversity among the myriad of threats. We identify eight major PII-breach types and provide initial literature reviews for each type of breach. We detail how US organisations differ regarding their exposure to these breaches, as well as how the level of severity (i.e. number of records affected) differs among these PII breaches. Finally, we offer several paths for future research.

In this paper, we define and validate a new multidimensional measure of Open Source Software (OSS) project survivability, called Project Viability. Project viability has three dimensions: vigor, resilience, and organization. We define each of these dimensions and formulate an index called the Viability Index (VI) to combine all three dimensions. Archival data of projects hosted at SourceForge.net are used for the empirical validation of the measure. An Analysis Sample (n=136) is used to assign weights to each dimension of project viability and to determine a suitable cut-off point for VI. Cross-validation of the measure is performed on a hold-out Validation Sample (n=96). We demonstrate that project viability is a robust and valid measure of OSS project survivability that can be used to predict the failure or survival of an OSS project accurately. It is a tangible measure that can be used by organizations to compare various OSS projects and to make informed decisions regarding investment in the OSS domain.

Abstract Installation of maintenance patches in operational software systems is a source of significant expenditure and resource consumption. Managers often have to find a balance between publicly announced vulnerabilities and/or possible destabilization of existing applications, while making decisions regarding patch roll out to all systems. We propose a classification scheme for maintenance patches and examine the effects of patch category on the internal characteristics of a software system. Text mining the patch releases of 77 successive versions of the Linux operating system, we extend previous categorization schemes to maintenance patches. This granularity level offers a view of the aggregate nature of the tasks performed in each version. An unsupervised learning technique, cluster analysis associated with Text mining, reveals that there are three identifiable categories in Linux patch files. Based on the maintenance keywords in each category, we label them as:corrective,perfectiveandadaptivepatches. Further analysis of the effects of patch category on the structural complexity and the time to next release indicates that perfective patches are associated with a reduction in the complexity and frequency of patch release. Categorization at the patch level is useful for managers, since changes made to operational software systems are through patches. Determining the nature of a patch can assist managers in planning version roll out and testing criterion. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract The Department of Information Systems, Statistics and Management Science, prediction of software defects and defect patterns is and will continue to be a critically important software evolution research topic. This study presents a time series analysis of multi‐organizational multi‐project defects reported during ongoing software evolution efforts. Using data from monthly defect reports for eight open source software projects over five years, this study builds and tests time series models for each sampled project. The resulting model accounts for the ripple effects of defect detection and correction by modeling the autocorrelation of code defect data. The autoregressive integrated moving average model (0,1,1) was found to hold for all sampled projects and thus provide a basis for both descriptive and predictive software defect analysis that is computationally efficient, comprehensible, and easy to apply. The model may be used to evaluate and compare the reliability of candidate software solutions, and to facilitate planning for software evolution budget and time allocation. Copyright © 2008 John Wiley & Sons, Ltd.