A Recommendation System for Planning Software Releases

Strategic release planning is a critical step in iterative software development. It involves assignment of requirements to subsequent releases in consideration of constraints and stakeholder demands. Manually analyzing release planning projects is challenging since large volumes of data are involved, and release planning models are dependent on several input parameters and complex algorithms. A recommendation system, called SRP-Plugin 2.0, is presented in this thesis to assist product managers with better release decisions. First, literature is reviewed systematically for related recommendation systems (contribution 1). SRP-Plugin 2.0 is realized using four techniques. Simulation and sensitivity analysis are utilized to determine the important input parameters (contribution 2). Machine learning is used for predicting the impacts on the release plans due to project changes (contribution 3). A recommendation method assists with achieving certain release planning targets (contribution 4). Finally, SPR-Plugin 2.0 is implemented as a plug-in for an integrated development environment (contribution 5)

Phishing in a university community: Two large scale phishing experiments

Phishing is a type of social engineering where a potential victim is sent a message that impersonates a legitimate source or organization. Phishing attacks typically lure the targets into revealing confidential information such as password, credit card details, bank account numbers, or any other sensitive information. Human behavior and technology are two equally important aspects of phishing attacks, while current anti-phishing research have focused on the technology front, very few real life studies have been performed with a focus on the human aspects of phishing attacks. In this paper, we present the results of two large scale real life phishing attacks conducted on more than 10,000 community members of a university that includes students, alumni, faculty and staff. Our study is the first large scale phishing experiment on human subjects. Previous work suggests that users\u27 demographics are useful indicators in identifying the most vulnerable users to phishing attacks. Our results illustrate that user demographics alone cannot predict user\u27s susceptibility to phishing attacks. We also found that warning users about phishing risks alone is not sufficient to prevent more users from responding to the phishing attack. Even though subjects were warned not to respond to phishing emails, many disregarded the warning. We explain our findings through analysis of the empirical results of the two real life phishing attacks conducted. © 2012 IEEE