A document by Sasibhushan Rao Chanthati. Click on the document to view its contents.

A document by Sasibhushan Rao Chanthati. Click on the document to view its contents.

A document by Sasibhushan Rao Chanthati. Click on the document to view its contents.

A document by Sasibhushan Rao Chanthati. Click on the document to view its contents.

A document by Sasibhushan Rao Chanthati. Click on the document to view its contents.

This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using machine learning and successfully filters out phishing emails from reaching the intended clients. This study also illustrates how to conduct email domain verification using MongoDB Atlas, which a database is containing approved vendors’ domains, to reduce spoofing. Specifically, incorporating NLP helps the system analyze raw data to categorize it and identify patterns potentially leading to a spoofing attempt, enhancing the spoofing detection and prevention of the system. The paper also presents arguments that require awareness and integration of new technologies in the security frameworks. Hence, incorporating machine learning, data science, and NLP presents robust, versatile, and cost-effective solutions to enhance cybersecurity and ultimately protect vital information and organizations’ monetary loss due to cybercrimes.

Testing is an important means to ensure the quality of software. With the increase of software change frequency, the efficiency of testing is required to be continuously improved. Among the many factors that affect the efficiency of software testing, the generation of test cases is very critical, and there is still a lack of effective methods. In this paper, a method of software test case generation driven by knowledge graph changes is proposed. Its purpose is to improve the efficiency of test case generation by using the change of knowledge graph to determine the test cases available for the changed software. Firstly, before and after the change of the software knowledge graph is constructed. Then, the changed entities and paths in the knowledge graph are identified, and the test cases available for the software after the change are filtrated by the similar entities. Meanwhile, the cases for testing the changed entities are generated by multi-objective optimization method. Finally, the above test cases are integrated to form a valid case set for testing the changed software. The proposed method is applied to six benchmark programs and compared with other algorithms. The experimental results demonstrate that the proposed method can significantly improve the efficiency and accuracy of test case generation.

Large language models (LLMs) have been touted to enable increased productivity in many areas of today’s work life. Scientific research as an area of work is no exception: the potential of LLM-based tools to assist in the daily work of scientists has become a highly discussed topic across disciplines. However, we are only at the very onset of this subject of study. It is still unclear how the potential of LLMs will materialise in research practice. With this study, we give first empirical evidence on the use of LLMs in the research process. We have investigated a set of use cases for LLM-based tools in scientific research, and conducted a first study to assess to which degree current tools are helpful. In this paper we report specifically on use cases related to software engineering; specifically, on generating application code and developing scripts for data analytics and visualisation. While we studied seemingly simple use cases, results across tools differ significantly. Our results highlight the promise of LLM-based tools in general, yet we also observe various issues, particularly regarding the integrity of the output these tools provide.

Atrial Fibrillation (AF) is a prevalent form of arrhythmia that primarily affects the atria (upper chambers of the heart), causing disorganized electrical signals and resulting in an irregular heartbeat. Electrocardiography (ECG) is the most commonly used technology for detecting AF, as ECG signals provide crucial information on cardiac irregularities, making it an essential tool for AF diagnosis. However, manually analyzing ECG signals is both mentally demanding and time-consuming. In this study, we propose Hybrid CNN-LSTM model to determine whether a given ECG signal represents a normal sinus rhythm or indicates Atrial Fibrillation. We utilize the Physionet Database for our dataset. The proposed hybrid model aims to assist doctors by significantly reducing the time and mental effort required for diagnosis, facilitating early detection of the disease and lowering the risk of thromboembolic events, including ischemic strokes. Our Hybrid CNN-LSTM model is designed to autonomously learn discriminative features from raw ECG signals, eliminating the need for manual feature extraction. We compared the performance of the Hybrid CNN-LSTM model with a standard CNN model. The Hybrid CNN-LSTM model achieved an accuracy of 95.2%, outperforming the CNN model, which achieved an accuracy of 81.4%. Therefore, our proposed model offers a more accurate method for

During the past decade, most global firms have adopted the global software engineering approach for outsourcing their software development projects to reduce their cost and time-to-market, enhance their access to the competitive market, and advance their technical skills and resources. This paradigm shift from conventional geographically-bound development to a globalized software industry has happened due to an abrupt improvement in information and communication technologies. One of the successful paradigms of global software engineering is offshore software development, which has turned into an extremely beneficial industry for many of its stakeholders. However, this industry is posed with many impeding factors that impede the successful completion of numerous outsourcing projects. In this study, we have detected several significant impeding factors faced by the outsourcing industry. We employed a Systematic Literature Review (SLR) methodology and utilized constructed search criteria originated from our anticipated research questions, followed by choosing the particular research articles, inclusion and exclusion criteria, data extraction, and data synthesis, which resulted in 16 impeding factors from 118 final selected papers that were published between 2010 and 2024. Among these, eight impeding factors were identified as Critical Impeding Factors (CIFs). Furthermore, the investigated impeding factors have been analyzed by considering different scenarios, such as region-wise, research strategy based, and organization size. Our results can play a vital role for the outsourcing industry in improving its procedures to enhance their communication strategies, contract negotiation and management processes, cultural and language issues, and project monitoring.

A document by Sasibhushan Rao Chanthati. Click on the document to view its contents.

This research focuses on the topic of using AI in color variant management in products to enhance the appeal and performance of the products in the marketplace, by incorporating artificial intelligence, deep learning, and neural network systems. Applying consumers and products information, preferences, buying history and sales history, WE created AI models to predict and change product colors in real-time. The complete workflow used comprises data gathering, processing, and feature extraction, model training, integration of the color adjustment software tools, and finally, testing and validation. The efficiency of such AI-driven interventions was analyzed through the consumer satisfaction indices, the sales results, and the engagement data based on the consumption of digital platforms. This study demonstrates valuable potential of AI to improve product design application and development while providing valuable suggestion for Business adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in the ways of enhancing futuristic consumer-oriented marketing approaches.

This article discusses the use of classification of web visitors and determination of branding quality using AI in effort to redesign digital marketing. AI in its various forms such as machine learning, natural language processing, and computer vision helps businesses to better understand their users' behavior, better understand the context of supplied content and improve user experience. The application of AI in the management of the websites offers features such as Real-time monitoring, automated content tuning, and Analytics for predictions. Automated tools can analyze who is visiting the site, what kind of work they are doing in terms of SEO, and how to assist with creating high-quality content. Also, AI helps with mass and targeted promotions like recommended products and services, variable rates of prices, etc. AI can help unlock significant benefits for businesses, across the board and lead to enhanced engagement in the digital space and thus gives a competitive advantage in the market. The article also discusses investment in training and education for consumers to ensure them remain relevant with emerging technologies in AI and the market. By way of illustration and analysis, the article clearly outlines how the incorporation of AI in organizational functions can bring about operational efficiencies and cost savings, as well as result in remarkable improvements in branding and marketing strategies.

Finding software developers with expertise in specific technologies that align with industry domains is an increasingly critical requirement. However, due to the ever-changing nature of the technology industry, locating these professionals has become a significant challenge for companies and institutions. This research presents a comprehensive overview of studies exploring suitable recommendation systems that can assist companies in addressing this pressing need. To conduct this study, we employ a hybrid systematic mapping approach with an initial number of 1251 studies and a final selection of 21 studies. Our work focuses on collecting data on key technologies, methodologies, and data sets utilized in proposed recommendation systems, to design a new recommendation system that can effectively identify specialists capable of aligning specific technical knowledge with industry domains. The outcomes of this study include insights into the current research trends in this field, alongside a practical overview of considerations necessary for developing a recommendation system that successfully meets the criteria for aligning technical skills with industry domains. By following a hybrid Systematic Mapping methodology and presenting the outcomes in the form of insights, this research addresses the challenge of finding software developers with domain-specific expertise in a rapidly changing technology industry, laying the groundwork for aligning technical skills with industry domains.

Background: Bidirectional data pipelines have emerged as a response to the evolving needs of modern data ecosystems. Traditionally, unidirectional pipelines allowed data to flow in a single direction, limiting interaction. The surge in demand for real-time bidirectional communication prompted the development of pipelines that enable two-way data flow, facilitating seamless and dynamic exchanges between source and destination. Objective: The research aims to delve into the role of bidirectional data pipelines within the companies producing and selling software-intensive embedded systems products. Further, the study endeavors to elucidate the fundamental differences between unidirectional and bidirectional data pipelines, shedding light on their unique characteristics. Through comprehensive exploration, it seeks to discern the benefits and challenges inherent in implementing and maintaining bidirectional data pipelines. Furthermore, a critical aspect of the research involves outlining the intricate steps and considerations essential for migrating from unidirectional to bidirectional data pipelines. This includes a focus on prerequisites, methodologies, and the potential benefits derived from such a transition. Method: This study employs a qualitative research approach centered around a multiple-interpretive case study to delve into the complexities of bidirectional data pipelines. Five distinct use cases have been meticulously selected to provide a comprehensive understanding of various aspects of bidirectional data pipelines. Through the in-depth analysis of these concrete use cases, this research aims to elucidate the intricacies, benefits, and challenges associated with bidirectional data pipelines in software-intensive embedded systems environment. Results: The study yielded insightful results on various aspects of bidirectional data pipelines, emphasizing their distinctions from unidirectional data pipelines without a shared data transmission channel. It uncovered the compelling need for bidirectional data pipelines in modern data-centric environments, where the dynamic exchange of information between source and destination is pivotal. The identified benefits ranged from enhanced real-time data synchronization to improved responsiveness in addressing evolving business requirements. Concurrently, the study elucidated inherent challenges, such as increased complexity in pipeline management and potential security considerations. Moreover, the research provided a nuanced understanding of the stepwise process involved in transitioning from unidirectional to bidirectional data pipelines. Conclusion: The study underscores the pivotal role of bidirectional data pipelines in meeting evolving data requirements, offering benefits like real-time synchronization and improved decision-making. Despite their importance, there is a lack of comprehensive research on bidirectional pipelines, prompting the need for further exploration. The transitional journey involves addressing challenges such as data consistency and security concerns while optimizing system design for compatibility. For researchers, the study suggests avenues for optimizing bidirectional pipeline performance, examining long-term impacts, scalability, and real-time anomaly detection. Practitioners can leverage insights for informed decision-making in transitioning to bidirectional data pipelines, aligning organizational needs with their benefits and challenges.

Context: Process Debt, like Technical Debt, can be a source of short-term benefits but often is harmful in the long term for a software organization. Nonetheless, information about Process Debt is scarce in the current literature. Objective: This paper aims to define Process Debt, describe its occurrence with associated risks, and show examples of mitigation strategies. Method: Firstly, we conducted an exploratory study of Process Debt in four international organizations by interviewing 16 practitioners. Then, we validated and extended the findings with a cross-company focus group with additional 10 practitioners. Finally, we analyzed 58 additional observations and 35 interviews from a longitudinal case study. Results: The findings show that Process Debt can be a harmful phenomenon that needs attention. We provide a framework and a definition, and report causes, consequences and occurrence patterns over time of Process Debt. We present mitigation strategies and which ones need further attention for future research. Conclusions: The debt metaphor may help companies understand how to manage and improve their processes and make process-related decisions that are beneficial both in the short and long term.

This paper proposes a higher-order transformation (HOT) for realizing Incremental Change Propagation (ICP) from software UML models extended with performance annotations to performance Layered Queueing Network (LQN) models. Such a transformation is necessary for integrating quantitative performance analysis into the model-driven engineering of real-time systems. The entire process starts by automatically generating an LQN and a trace model from a UML model extended with MARTE annotations, with a batch Epsilon ETL transformation previously developed by the authors. The textual ETL transformation definition is translated to an ETL transformation model using the Epsilon Haetae tool. The ETL transformation model conforms to the ETL metamodel and represents the mapping between source and target models at a high level of abstraction. We use it to answer the question: what needs to be changed in the target model upon detecting changes in the source model? During the development process, when the UML model evolves, we detect such changes with the Eclipse EMF Compare tool, then incrementally propagate them to the LQN model to keep it synchronized. The extended approach is illustrated by applying it to an e-commerce model from the literature. The execution time of ICP is measured and compared to the traditional batch transformation.

Background: Given the enormous impact that the software industry has on so many facets of our lives today, software projects are extremely important. Digital transformation is driven across industries by software developments. They facilitate the utilisation of technology by organisations to optimise operations, augment productivity, and provide inventive products and services. To be competitive and adjust to the quickly changing market dynamics of today’s world, companies must undergo digital transformation by making full use of software projects. Objectives: The main aim of the current study is to determine the main causes of project delay and cost overrun in software projects, identify the effects of project delay and cost overrun on software projects, and define the role of cost estimation models in overcoming project delays and cost overruns in these projects. Results and conclusions:The researcher reviews the literature on causes behind delay and cost overrun and describes the role that can be played by cost estimation models in that process. In light of the reviewed literature, the researcher reaches certain recommendations for the software industry.