Community question-answering (CQA) sites are popular as information-seeking platforms where users communicate to their peers. Security-related posts are gaining popularity with the rapid development of information technology in these sites and. CQA sites contains wide range of posts from classic cryptography to recently popular mobile security. Investigating such posts can be useful for researchers, teachers and developers. In this article, spectral clustering and frequent term-based summarization techniques are proposed for security related posts. The proposed method is developed in three stages. In the first stage, security related folksonomies are created and security post profile matrix is built with the help of tag frequency-inverse security post frequency. In the second stage, security related posts are grouped with help of spectral clustering algorithms. Finally, in the third stage, frequent terms are extracted from each cluster for security related post summarization with the help of frequent words and semantic similarity.

Software bug records are stored and managed using bug tracking tools. A software bug is characterized by a number of attributes like bug id, opened date, closed date, reported by, assigned to, summary (title), description and set of comments. Summary and description are the two important attributes of a bug. Description gives the detailed information about a bug, whereas summary (title) of a bug gives a quick glance and short information about a bug. The objective of this study is to discover the relationship between description and summary attributes of a bug and to find whether summary of a bug is really the compact and intelligent information of description of a bug. This finding helps in providing a new direction for faster knowledge discovery in a bug repository. Another objective of the work is to demonstrate that intelligent summary of a bug can be generated from description of bug using topic modeling techniques. In this work, topic modeling techniques are used to generate meaningful terms for framing the bug summary of software bugs which can be utilized for faster knowledge discovery. Topic modeling techniques can be utilized efficiently for generating intelligent summary from description of a software bugs and then the knowledge discovery can be performed using the intelligent summary only since it will reduce the volume of data for knowledge discovery. To demonstrate the presented approach, experiments are performed on three popular bug repositories namely, Android, Mozilla and MySql. Comparative analysis is carried using various performance parameters and in order to analyze the impact of present work, two knowledge discovery tasks namely, bug classification and duplicate bug identification are presented in this study.

A similarity measure namely, similarity measure for text processing (SMTP) is proposed by Lin et al. [1] for knowledge discovery on text collection. The proposed measure considered the three cases for similarity measurements between the pairs of documents. These cases are based on absence and presence of features in the pair of text documents. The first case covers the features appearing in both of the documents, second case covers the features appears in only one document and the third case covers the features appears in none of the documents. The proposed similarity measure considered to be ideal for finding similarity between the pair of text documents on the basis of presence or absence of features available in text documents, however, while exploring the SMTP similarity measurement it is found that the case of measuring similarity between the pair of similar documents is not covered. The objective of this work is to highlight this gap and propose a minor change to make the SMTP a complete similarity measurement technique for knowledge discovery in line with the other standard similarity techniques.

The performance of ten classic algorithms to classify the software bugs for different bug repositories are compared. The algorithms included in the study are Naïve Bayes, Naïve Bayes Multinomial, Discriminative Multinomial Naïve Bayes (DMNB), J48, Support Vector Machine, Radial Basis Function (RBF) Neural Network, Classification using Clustering, Classification using Regression, Adaptive Boosting (AdaBoost) and Bagging. These algorithms are applied on four open source bug repositories namely Android, JBoss-Seam, Mozilla and MySql. The classification is evaluated using 10-fold cross validation technique. The accuracy and F-measure parameters are compared for all of the algorithms. The concept of software bug taxonomy hierarchy is also introduced with eleven standard bug categories (classes). The comparative study also covers the effect of number of categories over performance of classifiers in terms of accuracy and F-measure. The results are produced in tabular and graphical forms.