The Definition of Intelligent Computer Aided Software Engineering (I-CASE) Tools

The growing complexity of the software systems being developed and the use of different methodologies indicate the need for more computer support for automating software development process and evolution activity. Currently, Computer-Aided Software Engineering (CASE), which is a set of software systems aimed to support set of software process activities, does this automation. While CASE tools prove its importance to develop high quality software, unfortunately CASE tools doesn’t cover all software development activities. This is because some activities need intellectual human skills, which are not currently available as computer software. To solve this shortcoming, Artificial Intelligence (AI) approaches are the ones that can be used to develop software tools imitating these intellectual skills. This paper presents the definition of Intelligent Computer Aided Software Engineering (I-CASE). The definition encompasses two steps. The first step is a clear decomposition of each basic software development activity to sub activities, and classify each one of them whether it is an intellectual or procedural job. The second step is the addressing of each intellectual (un-automated) one to proper AI-based approach. These tools may be integrated into a package as an Integrated Development Environment (IDE) or could be used individually. The discussion and the next implementation step are reported. Keywords: Software Engineering, CASE tools, Artificial Intelligenc

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

Abstractive Summarization with Efficient Transformer Based Approach

One of the most significant research areas is how to make a document smaller while keeping its essential information because of the rapid proliferation of online data. This information must be summarized in order to recover meaningful knowledge in an acceptable time. Text summarization is what it's called. Extractive and abstractive text summarization are the two types of summarization. In current years, the arena of abstractive text summarization has become increasingly popular. Abstractive Text Summarization (ATS) aims to extract the most vital content from a text corpus and condense it into a shorter text while maintaining its meaning and semantic and grammatical accuracy. Deep learning architectures have entered a new phase in natural language processing (NLP). Many studies have demonstrated the competitive performance of innovative architectures including recurrent neural network (RNN), Attention Mechanism and LSTM among others. Transformer, a recently presented model, relies on the attention process. In this paper, abstractive text summarization is accomplished using a basic Transformer model, a Transformer with a pointer generation network (PGN) and coverage mechanism, a Fastformer architecture and Fastformer with pointer generation network (PGN) and coverage mechanism. We compare these architectures after careful and thorough hyperparameter adjustment. In the experiment the standard CNN/DM dataset is used to test these architectures on the job of abstractive summarization