This paper proposes a pilot approach based on the comparative analysis of supervised Machine Learning models coupled with basic Natural Language Processing concepts for classifying Functional and Non-Functional Requirements from huge collections of data relevant to the Requirements Engineering (RE) phase within software development. The publicly available PROMISE Software Engineering Repository dataset is used in the execution of this approach. Non-Functional Requirements are further classified into subclasses based on attributes they address since they are not directly related to the core functions of the concerned software. This overall research initiative helps to make the RE phase more efficient and reduces human effort in software development. It leverages Big Data in Software Engineering.

In recent years, several text mining models have been developed for supporting the process of early and accurate cancer diagnosis. This study provides a comparative analysis of existing text mining algorithms for cancer diagnostics. This study will support researchers and practitioners when choosing the most accurate algorithm when diagnosing specific types of cancer.

Cloud computing has become ubiquitous in the modern world and has offered a number of promising and transformative technological opportunities. However, organizations that use cloud platforms are also concerned about cloud security and new threats that arise due to cloud adoption. Digital forensic investigations (DFI) are undertaken when a security incident (i.e., successful attack) has been identified. Forensics data collection is an integral part of DFIs. This paper presents results from a survey of existing literature on challenges related to forensics data collection in cloud. A taxonomy of major challenges was developed to help organizations understand and thus better prepare for forensics data collection.

We developed a novel Vertical Breadth-First Search All-path graph algorithm that utilizes vertical data structures to find all-length paths (including shortest paths) for all pairs of vertices in a graph. In the current research we propose an approach to apply our All-path algorithm to perform Change Impact Analysis during Requirements Engineering. This article presents a roadmap of our future research plan to apply our All-path algorithm for software quality improvement.

Over the past few years, we have worked on pioneering an approach that employs Commonsense Knowledge (CSK) to automate the identification of Implicit Requirements (IMRs) from text in large Software Requirements Specifications (SRS) documents. This paper builds on our IMR-identification approach by adding CNN-based deep learning to detect IMRs from complex SRS big data such as images and tables.