Abstract: Software fault prediction models are created by using
the source code, processed metrics from the same or previous version
of code and related fault data. Some company do not store and keep
track of all artifacts which are required for software fault prediction.
To construct fault prediction model for such company, the training
data from the other projects can be one potential solution. Earlier we
predicted the fault the less cost it requires to correct. The training
data consists of metrics data and related fault data at function/module
level. This paper investigates fault predictions at early stage using the
cross-project data focusing on the design metrics. In this study,
empirical analysis is carried out to validate design metrics for cross
project fault prediction. The machine learning techniques used for
evaluation is Naïve Bayes. The design phase metrics of other projects
can be used as initial guideline for the projects where no previous
fault data is available. We analyze seven datasets from NASA
Metrics Data Program which offer design as well as code metrics.
Overall, the results of cross project is comparable to the within
company data learning.
Abstract: In this paper we are presenting some spamming
techniques their behaviour and possible solutions. We have analyzed
how Spammers enters into online social networking sites (OSNSs) to
target them and diverse techniques used by them for this purpose.
Spamming is very common issue in present era of Internet
especially through Online Social Networking Sites (like Facebook,
Twitter, and Google+ etc.). Spam messages keep wasting Internet
bandwidth and the storage space of servers. On social networking
sites; spammers often disguise themselves by creating fake accounts
and hijacking user’s accounts for personal gains. They behave like
normal user and they continue to change their spamming strategy.
Following spamming techniques are discussed in this paper like
clickjacking, social engineered attacks, cross site scripting, URL
shortening, and drive by download. We have used elgg framework
for demonstration of some of spamming threats and respective
implementation of solutions.