Application of Natural Language Processing in Object Oriented Software Development

Phishing is networked theft in which the main motive of phishers is to steal any person’s private information, its financial details like account number, credit card details, login information, payment mode information by creating and developing a fake page or a fake web site, which look completely authentic and genuine. Nowadays email phishing has become a big threat to all, and is increasing day by day. Moreover detection of phishing emails have been considered an important research issue as phishing emails have been increasing day by day. Various techniques have been introduced and applied to deal with such a big issue. The major objective of this research paper is giving a detailed description on the classification of phishing emails using the natural language processing concepts. NLP (natural language processing) concepts have been applied for the classification of emails, along with that accuracy rate of various classifiers have been calculated. The paper is presented in four sections. An introduction about phishing its types, its history, statistics, life cycle, motivation for phishers and working of email phishing have been discussed in the first section. The second section covers various technologies of phishing- email phishing and also description of evaluation metrics. An overview of the various proposed solutions and work done by researchers in this field in form of literature review has been presented in the third section. The solution approach and the obtained results have been defined in the fourth section giving a detailed description about NLP concepts and working procedure.

This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation.The NLP is a branch of computer science, AI and linguistics that focuses on the interaction between computer programs and human language. There are three functions of the NLP, namely, machine translation (MT), text summarisation and sentiment analysis (SA). MT is a branch of computer linguistics that analyses the use of computerised tools to translate the context derived from this, from one language to the next. The language that is being translated is the human language.Automatic text summarisation refers to a process of bringing about a succinct and meaningful summary of the text. SA is a way of finding out the polarity or strength of the opinion that is expressed in written text. The opinion could be either positive or negative. MT has three major approaches, namely, rule-based MT, statistical MT and neural MT. A rule-based system would typically require the expert to know both the source and the target languages. This knowledge is then used to develop syntactic, semantic and morphological rules to achieve the translation. The statistical approach uses statistical models based on the analysis of bilingual text corpora. The neural MT approach uses neural networks to achieve machine translation. There are two broad categories of text summarisation approaches, namely, extractive summarisation and abstractive summarisation. Furthermore, there are four popular types of SA, namely, the fine-grained SA, emotion detection, aspect-based SA and multilingual SA. The firm that deploys the NLP and ML in Big Data Analytics would gain crucial and better insights, setting the firm apart from its competitors.

An Ameliorated Methodology for the Abstraction of Object Oriented Features from Software Requirements Specification

Natural language is most common way to communicate with each other but sometime we can't understand other languages to understand different languages machine translation is needed. Machine translation is the best application which helps to understand any other language in less cost and less time. In this some problems are faced by researchers: words which pronounce same but having different meaning, some words spelled different but having same meaning, in some cases combination of words may change the meaning. To resolve such kind of problems Word Sense Disambiguation is needed. Word Sense Disambiguation is used to understand the correct meaning of the word with respect to context in which that is used; Word Sense Disambiguation is the part of natural language processing. Word Net plays an important role in Word Sense Disambiguation. In this paper, we will discuss about the basic concept of Natural language processing, Machine Translation and Word sense disambiguation.

Software requirements analysis is crucial for any software project and it is the basis of requirements reuse within Software Product Line engineering. Software requirements specifications are usually expressed in natural language, which are informal, imprecise and ambiguous, thus analyzing them automatically is a challenging task. Although methods towards automatic analysis of software requirements have been studied before, many of them have limitations and effective researches in this area are still lacking. Therefore, in this paper a new approach was proposed to automatically extract structured information of functional requirements from Software Requirements Specifications in natural language. The methods of machine learning, natural language processing and semantic analysis were employed and combined in this approach. With a 10-fold cross validation setting, the method was evaluated on a manually annotated corpus. The experiments show that this approach achieves a good performance. Moreover, it was found that the model trained on the requirements dataset of the E-commerce systems, can be used to extract semantic information from the requirements of auto-maker systems. The cross-domain extraction results show that the method of this paper is domain independent and robust to some extent.

Enhancing Cross-Linguistic Image Caption Generation with Indian Multilingual Voice Interfaces using Deep Learning Techniques

Information technology software tools are used in all walks of life. These software are developed using appropriate process models that are built on software development life cycle (SDLC) stratiform stage activities in linear or nonlinear order. These stratiform stages transform the requirements of ensuing software from general to human and machine understanding. The requirements gathered in the form of software requirements specification (SRS) of ensuing software is always bidirectional between each pair of end users and is non-sequenceable. This information flow is captured in the form of graph. The end software executed by machine is in a control flow-ordered segments of sequel of statements with one start and many ends depending on values of branching attributes analogical to hierarchical tree structure. Thus, the streamlining process should be in the direction from graph to tree structure. This paper, attempts to transform the quintessential part of information flow of the requirements present in SRS linguistically, reaching directionally and with respect to business aspect towards the stratiform analysis stage. The correctness and completeness of the transformation is authenticated through the maintenance of the scope.

Minangkabau language (ML) is one of the daily communication tools used by the people of West Sumatra, Indonesia. ML is a challenge in communicating. The ML language translation process is necessary to facilitate communication. This study aims to build a translation system for ML into Indonesian by developing the concept of natural language processing (NLP). NLP development adopts the performance of morphology-based Minangkabau language stemming algorithm (MLSA) which can separate basic words with affixes and endings. The research dataset adopts 600 basic ML words sourced from the big Minangkabau dictionary. The results of this study provide analytic output that can translate ML into Indonesian well. These results are presented based on the testing process on basic word input with an accuracy rate of 97.16% and based on text documents of 91.65%. Thus, the MLSA performance process presents the accuracy of the translation process. Based on these results, this research contributes to developing a stemming algorithm model in carrying out the process of removing prefixes, inserts, and suffixes in the Minangkabau language. Overall, this research can be useful as a tool for translating the ML into Indonesian.

In the Software Development Life Cycle, modelling plays a most significant role in designing and developing software efficiently. Acceptance Test-Driven Development (ATDD) is a powerful agile software development model where a customer provides user acceptance test suits as a part of Software Requirements Specifications. A design has to develop a system so that User Acceptance Tests will be successful. In some systems, the Combinatorial Logic and Combinatorial Testing play a very crucial role. The authors have proposed a novel approach to enhance the existing Acceptance Test Driven Development model to Combinatorial Logic Oriented-ATDD model by incorporating combinatorial logic. Refinement with respect to combinatorial logic needs to be incorporated in all the stages of Software Development Life Cycle, i.e. starting from Software Requirement Specifications to User Acceptance Tests. This comprehensive approach derives the acceptance tests from user requirements effectively and efficiently. In this paper, the existing Indian Railway Reservation System is considered as a case study, and it was fully implemented as per proposed Combinatorial Logic Oriented-ATDD model.

A key part of software evolution and maintenance is the continuous integration from collaborative efforts, often resulting in complex traceability challenges between software artifacts: features and modules remain scattered in the source code, and traceability links become harder to recover. In this paper, we perform a systematic mapping study dealing with recent research recovering these links through information retrieval, with a particular focus on natural language processing (NLP).Our search strategy gathered a total of 96 papers in focus of our study, covering a period from 2013 to 2021. We conducted trend analysis on NLP techniques and tools involved, and traceability efforts (applying NLP) across the software development life cycle (SDLC). Based on our study, we have identified the following key issues, barriers, and setbacks: syntax convention, configuration, translation, explainability, properties representation, tacit knowledge dependency, scalability, and data availability.Based on these, we consolidated the following open challenges: representation similarity across artifacts, the effectiveness of NLP for traceability, and achieving scalable, adaptive, and explainable models. To address these challenges, we recommend a holistic framework for NLP solutions to achieve effective traceability and efforts in achieving interoperability and explainability in NLP models for traceability.

Estimation of Software Development Effort from Requirements Based Complexity

Requirements Engineering (RE) is concerned with the gathering, analyzing, specifying and validating of user requirements that are documented mostly in natural language. The artifact produced by the RE process is the software requirements specification (SRS) document. The success of a software project largely depends on the quality of SRS documentation, which serves as an input to the design, coding and testing phases. This paper approaches the problem of the automatic quality assessment of textual requirements from an innovative point of view, namely the use of the Natural Language Processing (NLP) text classification technique. The paper proposes a quality model for the requirements text and a text classification system to automate the quality assessment process. A large study evaluating the discriminatory power of the quality characteristics and the feasibility of a tool for the automatic detection of ambiguities in requirements documentation is presented. The study also provides a benchmark for such an evaluation and an upper bound on what we can expect automatic requirements quality assessment tools to achieve. The reported research is part of a larger project on the applicability of NLP techniques to assess the quality of artifacts produced in RE.

A requirement is a feature of the system or a description of something the system is capable of doing in order to fulfill the system's purpose. Building a data warehouse is a very challenging task. Most of the data ware house project fails to meet the business requirements and business goals because of the improper requirement engineering phase. The Requirement Engineering (RE) is the most important phase of the software development life cycle (SDLC). This phase results into a large document written in natural language that converts the incomplete, imprecise requirements and wishes of the potential users into complete, precise and formal specifications. This formal specification is known as SRS i.e. Software Requirement Specification. These Requirements are transformed into information package in the form of dimensions, hierarchies and facts to build a data warehouse. Hospital can be seen as an example of a complex system. This paper considers Hospital as a case study for which a software system has been developed taking the mentioned approach into account. In this paper we study information package for Hospital Automation System.

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

Search engines can return ranked documents as a result for any query from which the user struggle to navigate and search the correct answer. This process wastes user's navigation time and due to this the need for automated question answering systems becomes more urgent. We need such a system which is capable of replying the exact and concise answer to the question posed in natural language. The best way to address this problem is use of Question answering systems (QAS). The basic aim of QAS is to provide short and correct answer to the user saving his/her navigation time. The concept of Natural Language Processing plays an important role in developing any QAS. This paper provides an implementation approaches for various categories of QAS such as Closed Domain based QAS, Open Domain based QAS, WEBBASED QAS, Information Retrieval or Information Extraction (IR/IE) based QAS, and Rule based QAS which will be helpful for new directions of research in this area.