Software development is a multifaceted endeavor, requiring a profound grasp of both social dynamics and technical intricacies. Poor collaboration often leads to the accumulation of social debt, manifesting as unforeseen project costs due to sub-optimal team interactions. Community smells have emerged as indicators of these socio-technical inefficiencies and potential social debt. While previous research has focused on automated detection of community smells through analyzing developer communication patterns, our study offers a complementary approach. We emphasize the critical role of project managers in assessing socio-technical dynamics and propose a novel, tool-supported catalog of symptoms. This catalog can be used for manual inspections to identify early signs of community smells at the individual level, allowing managers to address issues before they escalate. Using a mixed-method design that leveraged an existing literature review and a user survey, we cataloged symptoms related to four community smell types. Additionally, we developed TOAST, a tool that operationalizes this catalog, and assessed its usability. The paper concludes by shedding light on the potential impact of our work and its contribution to advancing the detection and analysis of community smells.

Modern software development requires constant code evolution, often leading to a misalignment between evolving source code and its associated test suite. This lack of synchronization can impact software quality and longevity. This research investigates the factors influencing test co-evolution and its impact on software development practices. Our methodology involved analyzing the evolution of test code proportions, employing clustering techniques to identify distinct evolution patterns, and classifying projects based on their level of test co-evolution using the Pearson Correlation Coefficient. Our findings revealed five distinct test evolution patterns, highlighting diverse approaches to integrating testing within development workflows. Furthermore, high test co-evolution correlated significantly with smaller development teams and a lower frequency of corrective and multi-maintenance activities, indicating a potential link between consistent test updates, early defect detection, and improved code quality.

As software artifacts continuously evolve and increase in number, the need for automated traceability increases due to the complexity of trace links. Besides tracing components across different artifacts, the need for tracing to application domains is critical to understand the classification of semantics and the coverage (i.e., which application domain is present in each artifact?). In this paper, we propose the notion of using NLP to map concepts emerging from software artifacts to application domains, and tracing these between artifacts. We extracted the corpus keywords from source code, documentation, and tests. We ran an optimised Latent Dirichlet Allocation (LDA) to generate the concepts emerging from each artifact. We then calculated the similarity scores of each concept against each application domain, and ranked the difference of these scores between pairwise artifacts. Results show that the ranking of the inverse of the difference represents the strength of tracing in semantics, and different embeddings show varying results. We observed the strong applicability of our method and its replicability by other researchers and practitioners, particularly in detecting synchronised application domains that are traced between artifacts.

An organic branding is one of the most unique processes to build a brand. Most of the companies are trying to build their brand using paid branding through some well-known platforms like Google, Facebook, etc. Sometimes this method may work, but it may not have a far-reaching effect. Creating some hype is easy with paid branding. Creating mass-trust is not possible with paid branding. Moreover, it is expensive. Every company want a lot of organic traffic. The high volume of traffic will increase the orders for a company. For example, search for any online services such as software service providing website or e-commerce online stores or job boards or any other goods, the main revenue growth depends on the amount of traffic routed to their website.

https://www.researchgate.net/publication/382955327_Product_Colour_Variation_Management_with_Artificial_Intelligencehttps://journals.e-palli.com/home/index.php/ajet/article/view/3213This 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. Real-time consumer and product information, preferences, buying history, and sales history; I created an AI model 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 suggestions for Businesses adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in ways of enhancing futuristic consumer-oriented marketing approaches. The paper was first completed in 2021 and later I have modified the paper with latest updates till date 2024.

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.

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.

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.

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.

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.

In recent years, with the updating and iterating of new global technologies, digital twins (DTs) originated in the aerospace industry has been widely used in smart manufacturing, smart construction, and smart management as a new type of informatization management tool. It is therefore important for scholars to understand the current state of research on DTs to conduct more targeted research in the future. Based on Scopus, 7384 publications related to DTs were found from 1951 to 2023 in this study. With VOSviewers, the major countries, authors, and institutions researching DTs globally were analyzed and discussed. In conclusion, the study found that since 2018, increasing numbers of scholars around the globe, especially in China and the United States, have conducted relevant research in relation to DTs. Manufacturing and construction are the most common application areas for DTs. Over the past three years, the integration of AI, IoT, cloud, and digital technologies has developed into a hotpot of research. Additionally, communication technologies such as 5G and 6G contribute to the development of DTs. In the future, it can be predicted that DTs will be utilizing machine learning, blockchain and other technologies as a result of advancements in becoming smarter and more secure over the years.

Cross-chain bridges are crucial mechanisms for facilitating interoperation between different blockchains, allowing the flow of assets and information across various chains. Their pivotal role and the vast value of assets they handle make them highly attractive to attackers. Major security incidents involving cross-chain bridge projects have been occurring frequently, resulting in losses of several billion due to cyber attacks. The diversity of vulnerability exploitation methods by hackers is vast, but not entirely untraceable. There are scarce research outcomes studying cross-chain bridge cyber incidents, and we have conducted a study based on the most recent cross-chain bridge security incidents. We introduce the working principles, components, and architecture of cross-chain bridges, explain the categorization mechanisms of the trust layer in cross-chain bridges, summarize four categories of hacker vulnerability exploitation techniques from real cases, and propose preventative measures for cross-chain bridge security.

Systems-of-Systems (SoS) are composed of multiple independent systems called constituents that, together, achieve a set of goals by means of emergent behaviors. Those behaviors can be deliberately planned as a combination of the individual functionalities (herein named as features) provided by the constituents. Currently, SoS engineers heavily rely on their own creativity and prior experience to combine the features and design the behaviors. However, the limitation of human perception in complex scenarios can lead to engineering sub-optimized SoS arrangements, potentially causing waste of the resources, sub-optimal services and reduction in quality. To handle the aforementioned issues, this article presents a machine learning-based mechanism for inferring/suggesting emergent behaviors that could be designed over a given set of constituents. An initial dataset was elaborated from a systematic mapping to feed the mechanism and a web-application was developed as a means for experts to evaluate this mechanism. Results revealed that the algorithm developed is capable of predicting feasible emergent behaviors for different sets of constituents and the system can be useful in the sense of aiding SoS engineers and experts in the bottom-up design of these behaviors.

Users of software applications use Issue Tracking Systems (ITSs) to file enhancement reports, which leads to a large quantity of user requests. Indeed, these reports have become an important source for software requirements because they help to continuously improve software applications. Usually, developers and maintainers evaluate them and decide which user reports can be accepted. However, enhancement reports are continuously being raised one after another, which makes this process time-consuming and labor-intensive. Timely handling and implementation of these enhancement reports can effectively improve user satisfaction and product competitiveness. Thus, research has focused on automated methods for predicting which enhancement reports are likely to be approved, to maximum the value derived from user reports. However, reported results of existing approaches are typically not good enough for practical use. In this paper, we propose a novel creator-profile-based method to explore dependencies among enhancements to improve the prediction performance. Firstly, we define the concept of a creator profile, including a general method of how to generate creator profiles from the data set. Then we explain how to employ creator profiles to the problem of enhancement report approval prediction. Finally, we evaluate our approach on 40,551 enhancement reports collected from ITSs. The experimental results indicate that our proposed approach greatly improve on existing state of the art, especially in predicting approved reports. For cross-application prediction, the accuracy is 80.7%, while for non-cross-application prediction, the overall accuracy is 83.6%. That is, with the proposed approach, over 80% of user requests can be automatically identified for exacting valuable user requirements, which significantly reduces labor costs. Replication package is available at: https://anonymous.4open.science/r/approval_prediction-507E

Many software development methodologies introduced to date place users at the centre of development process. Although the user is an important asset in the software development cycle however the user-centred approach is not sufficient to develop a software product that is structurally robust and reliable. User involvement in the development process does not always guarantee resilience and a more efficient design. To address this a hybrid-based software development paradigm is proposed here where the software development cycle includes a grammar model-based compiler with user-centred approach. The efficacy of the proposed system is tested with the development of an innovative computer-aided drawing technology (SETUP09) for blind and visually impaired people and the results are compared with an existing non-hybrid-based drawing software (IC2D). The results of this study confirm SETUP09 improves user satisfaction and provides abstract and concrete level system flexibility. Provided here are guidelines of the proposed hybrid approach for software development based on a formal approach. This hybrid approach enables the software designer to evaluate the software semantics before user scrutinization. The benefits of the approach include the facilitation of alternate development pathways affording the software designer the flexibility to amend the software without incurring significant technical challenges. In fact, the proposed approach enables the creation of a structural design that is independent from the development pathways. This approach should ease the time constraint in product development and resource constraints.

Software organizations are increasingly embracing the advantages of Global Software Development (GSD), such as access to highly skilled developers and reduced development costs. However, the implementation of Requirement Change Management (RCM) activities in GSD is often hindered by a lack of communication and coordination among project stakeholders, as well as an insufficient focus on traceability and monitoring of RCM activities. To overcome these issues, we have enhanced and improved an existing RCM framework to mitigate the identified challenges and to develop a quality product while achieving customer satisfaction and business objectives. To evaluate the effectiveness of the proposed Enhanced AZ-Model, we sought feedback from the industrial experts and performed a statistical analysis of the collected data. The Enhanced AZ-Model was further validated by performing simulations of the model. The empirical and simulation results indicate that the Enhanced AZ-model efficiently and effectively manage the demanded changes according to the budget and time constraints.

Binary similarity detection determines whether two given binary code snippets are similar or not, usually on function granularity. This task is challenging due to different compilation optimizations and CPU architectures. Recently, deep-learning methods have made great achievements in this field, although most of them use artificially selected features or ignore some important semantic information like code literals or function signatures during feature processing. In addition, random samples and pair loss function are used in similarity training, which only covers limited similarity relations between functions. In this paper, a new framework MFEN-Sim is proposed to detect similar binary functions. The framework contains three stages: feature extraction and normalization, mutli-feature based function feature embedding network (MFEN) and similarity learning network. Multiple features including assembly instructions, CFG structures and function code literals are extracted from binary functions. Then these features are fed into MFEN composed of three modules: function semantic and structure embedding module, function signature prediction module, and function code literal embedding module. The three modules generate embeddings representing the function semantic and structural features, the function signature prediction features and the function code literal features. Finally, MFEN-Sim utilizes a similarity training network based on contrastive learning to make MFEN recognize more similarity relations between functions. MFEN-Sim is evaluated on 281,601 functions in 144 binaries and 17 CVEs in real-world software. Experimental results show that our work outperforms state-of-the-art systems ( i.e., Gemini, FIT and SAFE) by 7.1%, 9.9% and 8.2% on AUC metric in cross-architecture, optimization-level similarity detection, and achieves higher recall than baselines in searching vulnerabilities in real-world applications.