Traditional Software Development Research Articles

Machine Learning Applications in Salesforce CPQ Transforming Sales

This paper aims to understand the innovative disposals of machine learning applications on Salesforce Configure, Price, Quote (CPQ) applications more precisely how these innovations are revolutionizing software development. Salesforce CPQ is an ideal solution for organizations that want a modern tool to help them with their sales processes, and adding machine learning elements significantly improves the system. For example, the study analyses different cases where and how machine learning algorithm is utilised in the application of the following areas: pricing strategy, product configuration and sales forecasting. Upon collection of historical data, machine learning models are able to make computations of patterns that enables businesses to make quick analysis of the best strategies to use in an offer of various prices to the customers in an effort to enhance customer satisfaction. Furthermore, the application of I/A in the context of CPQ decreases the time engaged in manual configurations, liberating software development teams to work on other priorities essentially. These difficulties and strategies are enumerated in the paper: data quality issues and the impossibility of the Machine learning successful implementation without cooperation with other departments, specifically with Salesforce CPQ teams. In addition, it shares its vision on the tendencies in application of machine learning in CPQ systems with reference to software development paradigms. Finally, the scope of this research is as follows: The study will examine how the utilization of machine learning applications in Salesforce CPQ has changed traditional software development practices in today’s dynamic market environment by increasing efficiency and accuracy effectively, improving competitiveness within the market.

Introducing rapid web application development with Oracle APEX to students of higher education

The rapid development of information and communication technologies (ICT), and enforcement of digital transformation have caused the need to develop and deliver software in even shorter time than previously, and has become crucial to businesses. Yet, this new need has caused a challenge of finding expert IT workforce able to deliver software applications due expected time. Moreover, in a rapidly changing business world, the traditional software development can no longer keep pace and provide solutions to businesses fast enough. Low-code development and inclusion of so-called citizen developers are seen as a solution for the problem. This paper explores the introduction of rapid application development courses on Oracle APEX technology to students of a European technological university. The paper discusses the implementation of the course and its design, and looks how the developed basic-level course was received by students. It is shown that the courses have been well-received by students, who based on provided feedback are satisfied with the online set up for self-regulated learning of rapid application development skills.

AI's Impact on Traditional Software Development

This paper explores the transformative impact of artificial intelligence (AI) on traditional software development practices. As AI technologies advance, they are reshaping various aspects of software development, including design, coding, testing, and maintenance. This paper examines how AI tools and techniques challenge conventional development methodologies, evaluates the benefits and limitations of AI integration, and discusses the implications for development teams and the industry at large. Through analysis and case studies, the paper provides insights into how AI is altering the landscape of software development.

Agile Methodologies in Mobile App Development for Real-Time Data Processing

In the rapidly evolving landscape of mobile app development, the need for real-time data processing has become increasingly critical. Traditional software development methodologies often struggle to keep pace with the dynamic requirements and quick turnarounds demanded by modern mobile applications. Agile methodologies, with their emphasis on iterative development, flexibility, and close collaboration, offer a promising solution for meeting these demands. This paper explores the integration of Agile methodologies in the development of mobile applications specifically designed for real-time data processing. Agile approaches, including Scrum, Kanban, and Extreme Programming (XP), are evaluated for their effectiveness in addressing the challenges of real-time data processing in mobile apps. The study emphasizes the importance of continuous feedback, rapid prototyping, and adaptive planning in ensuring that mobile applications can efficiently process and deliver data in real time. Furthermore, the paper discusses how Agile's incremental delivery model allows development teams to respond swiftly to changing user needs and technological advancements, thereby enhancing the overall performance and user experience of mobile apps.

Agile Methodologies in Mobile App Development for Real-Time Data Processing

In the rapidly evolving landscape of mobile app development, the need for real-time data processing has become increasingly critical. Traditional software development methodologies often struggle to keep pace with the dynamic requirements and quick turnarounds demanded by modern mobile applications. Agile methodologies, with their emphasis on iterative development, flexibility, and close collaboration, offer a promising solution for meeting these demands. This paper explores the integration of Agile methodologies in the development of mobile applications specifically designed for real-time data processing. Agile approaches, including Scrum, Kanban, and Extreme Programming (XP), are evaluated for their effectiveness in addressing the challenges of real-time data processing in mobile apps. The study emphasizes the importance of continuous feedback, rapid prototyping, and adaptive planning in ensuring that mobile applications can efficiently process and deliver data in real time. Furthermore, the paper discusses how Agile's incremental delivery model allows development teams to respond swiftly to changing user needs and technological advancements, thereby enhancing the overall performance and user experience of mobile apps.

Research on software reliability enhancement methods based on iterative development

Iterative development is an agile software development methodology that speeds up the development process and increases the flexibility of software delivery. However, the current software requirements change extremely fast, which leads to its increasing uncertainty, and iterative development also faces the challenge of software reliability. The software reliability enhancement method based on iterative development is researched and discussed for the current situation of insufficient reliability in the traditional software development process. In traditional software development, one-time delivery pressure and overall design complexity often lead to software functional defects and instability, affecting software reliability. The iterative development method, on the other hand, improves software reliability by decomposing the software development process into multiple iterative cycles, each of which contains requirements analysis, design, coding, and testing, updating the software functionality in smaller increments, and gradually improving the software product. This study proposes a software reliability improvement methodology by exploring and analyzing the key factors related to reliability in the iterative development process. This dissertation can establish a reliable and high-quality software development process through the steps of meticulous requirements planning and management, iterative development and testing, automated testing and continuous integration, code quality control, defect management and continuous improvement, user feedback and user engagement, and continuous learning and technology enhancement.

Automatic Code Generation for Android Applications Based on Improved Pix2code

With the expansion of the Internet market, the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle, tedious work and difficult system maintenance. Therefore, to improve software development efficiency, this study uses residual networks and bidirectional long short-term memory networks to improve the Pix2code model. The experiment results show that after improving the visual module of the Pix2code model using residual networks, the accuracy of the training set improves from 0.92 to 0.96, and the convergence time is shortened from 3 hours to 2 hours. After using a bidirectional long short-term memory network to improve the language module and decoding layer, the accuracy and convergence speed of the model have also been improved. The accuracy of the training set grew from 0.88 to 0.92, and the convergence time was shortened by 0.5 hours. However, models improved by bidirectional long short-term memory networks might exhibit over-fitting, and thus this study uses Dropout and Xavier normal distribution to improve the memory network. The results validate that the training set accuracy of the optimized bidirectional long short-term memory network remains around 0.92, but the accuracy of the test set has improved to a maximum of 85%. Dropout and Xavier normal distributions can effectively improve the over-fitting problem of bidirectional long short-term memory networks. Although they can also decrease the model’s stability, their gain is higher. The training and testing accuracy of the Pix2code improved by residual network and bidirectional long short-term memory network are 0.95 and 0.82, respectively, while the code generation accuracy of the original Pix2code is only 0.77. The above data indicates that the improved Pix2code model has improved the accuracy and stability of code automatic generation.

BlastAsia – its quest for innovation in enterprise software application development

ABSTRACT The case sheds light on the rapid evolution in the no-code application development space. It presents opportunities for entrepreneurs looking to support customers with their digital transformation journeys. The protagonist of the case, Arup Maity, is the founder of a traditional software development company, BlastAsia. He is contemplating launching a no-code application development platform, given evolving market preferences and customer expectations. The case outlines the emergence of no-code application development platforms as a potent alternative to traditional software development techniques. It highlights exactly how business uncertainty coupled with rapidly evolving technology requirements demands shorter software development lifecycles, which traditional methods might not be able to cater to. It also explores evolving customer and employee expectations to engage in applications which are intuitive, work across devices seamlessly, and deliver real-time project status and data. The case also discusses budgetary considerations, which companies looking to deploy enterprise software have to grapple with. The landscape is further complicated by cutthroat competition in the industry, start-ups offering enhanced value propositions at competitive prices, and black-swan events like the COVID-19 pandemic, which pose existential challenges for organizations.

Implementation of Web-Based School Library Information System Design with RAD Method Approach

<p>In the current era, several school libraries have implemented the design of web-based school library information systems using the Rapid Application Development (RAD) approach. The primary objective of this method is to enhance efficiency and accessibility in managing an organization/institution. This research aims to ascertain the advantages of the RAD method in the development of school library information systems. The research methodology employs a literature review by gathering relevant literature materials about the topic. The RAD method was chosen because it allows for rapid and iterative development, aligning with the dynamics of changing user needs and technology. The system is designed to support collection management, borrowing, and book searching effectively. The analysis results indicate that the implementation of the RAD Method in designing library information systems yields several positive impacts on the development of school library websites, namely, firstly, providing quick responses to user needs; secondly, improving previous systems; and finally, achieving relatively faster development compared to traditional software development methods.</p>

Automating the training and deployment of models in MLOps by integrating systems with machine learning

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity. This paper focuses on the importance of automated model training, and the method to ensure the transparency and repeatability of the training process through version control system. In addition, the challenges of integrating machine learning components into traditional CI/CD pipelines are discussed, and solutions such as versioning environments and containerization are proposed. Finally, the paper emphasizes the importance of continuous monitoring and feedback loops after model deployment to maintain model performance and reliability. Using case studies and best practices from Netflix, the article presents key strategies and lessons learned for successful implementation of MLOps practices, providing valuable references for other organizations to build and optimize their own MLOps practices.

Automating the training and deployment of models in MLOps by integrating systems with machine learning

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity. This paper focuses on the importance of automated model training, and the method to ensure the transparency and repeatability of the training process through version control system. In addition, the challenges of integrating machine learning components into traditional CI/CD pipelines are discussed, and solutions such as versioning environments and containerization are proposed. Finally, the paper emphasizes the importance of continuous monitoring and feedback loops after model deployment to maintain model performance and reliability. Using case studies and best practices from Netflix, the article presents key strategies and lessons learned for successful implementation of MLOps practices, providing valuable references for other organizations to build and optimize their own MLOps practices.

Discussion on How to Innovate and Improve the Computer Software Engineering Management

In the current rapidly changing information technology environment, computer software engineering management is facing many challenges. The traditional software development model is inefficient and difficult to adapt to frequent changes in demand; weak project management ability, leading to problems such as schedule delay and overspending; cross-departmental collaboration is inefficient, which affects the overall development process; the emergence of emerging technologies raises new requirements for management. To address these challenges, this paper proposes a series of innovative strategies. Through the implementation of these strategies, it is expected that the software engineering projects can improve the development efficiency, ensure the delivery quality, adapt to the changing market demand, and win a competitive advantage for enterprises.

Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

Objective: To create an AI-powered recommendation system that is designed for IT professionals to help them choose the best software development approaches. Through the use of specified data parameters. Methods: The recommendation system will make use of machine learning algorithms and data analysis methods to examine team dynamics, project needs, and other variables. The technology will enable developers to improve the quality of products and speed up the development process by recommending suitable development methodologies. Data parameters considered for the development of the recommendation model fall into four categories: requirements, user involvement, development team, type of project, and risk associated with it. Findings: Existing recommendation systems developed by different researchers are applicable for only requirement elicitation and to recommend different phases of the development process, whereas systems that will help select development methodology are not available in the existing systems. Among the five machine learning algorithms applied in the recommender system building process, the DecisionTree Classifier and RandomForest Classifier exhibit superior performance, achieving 100% accuracy, while the Kneighbors Classifier indicates 94.74% accuracy. Novelty: This study of systems introduces a novel approach to software development methodology, a recommender system, which helps IT developers select the best appropriate development approach for the development of a software product or project based on the type of project to be built and other data parameters. Keywords: Agile, Development, Requirements, Methodology, User, Customer

Application of task processing system based on wireless sensor network in English writing intelligent assistance platform

With the widespread use of 32-bit embedded task processing systems, embedded systems are used in various fields. Traditional software development is gradually being replaced by development methods based on embedded operating systems. The built-in processing system inherits the entire operating system and retains its core components, including thread synchronization and exclusive access to individual resources. However, embedded system resources are relatively limited, and operating requirements are more complex and flexible, which makes the development of embedded operating systems more complicated and difficult than general PC systems. The corpus as the basic intelligent corpus is the research capital used by scientists to study language, and it is also an important part of English writing teaching. In fact, after European linguists represented by Jeffrey Ritchie proposed the application of smart corpus to the teaching of English writing, smart corpus entered the field of view of scientists. This means that smart corpus will become an indispensable part of linguistics. This research method expands the research method of English writing, covering two main aspects: speech output and language error analysis. The application of this research method to the teaching system of English writing is mainly reflected in “language error analysis”. This article discusses corpus-based English writing. In addition, in order to enrich the application influence of the English teaching management system, the system discussed in this article aims at the optimization of the teaching system and has designed more useful functions (such as English broadcasting, personal center users, system settings, etc.), so that students can learn more. It is easy to record their English learning status and enrich the content of English courses.

Software Engineering in Nigeria: Trends and Challenges

Bringing theory and practice together is aspiration of software engineering education, so that the learner may acquire a deep understanding on basic concepts and principles, along with skills and competences to solve real-world problems. Thus, it is not just an academic concern about teaching relevant topics, but also a task to shape skilled individuals equipped for the industry demand. Moreover, due to the rapid development the software industry is and transnational nature, several challenges are challenging the qualification of software engineers, capable to develop products according to the international standards into markets overseas. Agile software development methods are the generally used and prominent software development methods. Agile methods are simple, easy and deliver software faster than the traditional software development methods. Software development in Nigeria is at developing stage.

Pervasive Computing For New Design In Big Data Analytics Software Development: Requirements Engineering Perspective

In recent years, the field of big data has been rapidly growing and has become a crucial aspect of many industries. The ability to collect, analyze, and interpret massive amounts of data has enabled businesses to make more informed decisions and has led to the development of new products and services. However, with the increase in data volume, velocity, and variety, traditional software development methods have become inadequate. This has led to the emergence of new design approaches that can handle the complexities of big data. Big data is very important nowadays for business organizations to make smart decisions based on insight gained from the big data. These benefit the organization to be competent in the market as well as to maximize their profit. However, there is a high rate of big data software project failure. This is due to a lack of new design approaches that can handle different dimensions of requirements for big data. The objective of this paper is to show how the pervasive computing / pervasive requirements engineering approach should be considered for big data software development based on different perspectives in pervasive environments to design new software

ScrumSpiral: An Improved Hybrid Software Development Model

In the lightning-quick world of software development, it is essential to find the most effective and efficient development methodology. This thesis represents "Scrum Spiral" which is an improved hybrid software development model that combines the features of Scrum and Spiral approach to enhance the software development process. This thesis aims to identify the usefulness of "ScrumSpiral" methodology and compare it with other hybrid software development models to encourage its use in software development projects. To develop this hybrid model, we did extensive research on the software engineering domain and decided to create a hybrid model by using Scrum and Spiral, named "Scrum Spiral" which is suitable for complicated projects and also for those projects whose requirements are not fixed. Traditional software development models face numerous challenges in rapidly changing markets. By developing this kind of hybrid model, we want to overcome these kinds of limitations and present the software development community with a novel concept for better project results. Final outcome of this thesis was that we developed a model that should be able to complete the project according to the expected schedule, satisfy customer requirements, and obtain productivity through team coordination. The significance of the hybrid model "Scrum Spiral" is reflected in its ability to offer flexibility towards various size projects, proactive risk management to identify all risks before developing the system, and result in higher-quality outcomes for those projects whose requirements are not properly described initially in the project.

Representation of some principles of the Agile Manifesto in the Semat Essence Kernel

The Agile Manifesto (AM) provides principles guiding agile software development as an alternative to traditional software development processes. While attempts have been madeto adapt processes to the agile context, representation of AM principles remains underdeveloped and subjective. The Semat (Software Engineering Method and Theory) Essence kernel offers a common ground for representing software development endeavors. In this paper, we represent some AM principles using the language provided by the Semat Essence kernel to establish a common ground. Additionally, we define constraints in OCL (Object Constraint Language) to enhance the Semat Essence kernel, enabling the introduction of time management in our representation. Such a representation will allow us for adapting and assessing different processes in an agile context.

Technical risk model of machine learning based software project development - A multinational empirical study using modified Delphi-AHP method

ContextThe development of machine learning (ML) based software projects has increased significantly over the past decade, introducing new technical risks that rarely or never appear in traditional software development projects. ObjectiveThis research aims to identify and prioritize the technical risk factors that may lead to the failure of ML-based software development projects. MethodFirst, a literature review was conducted to compile a preliminary list of technical risk factors for ML-based software project development. Then, two rounds of the modified Delphi process were conducted with 17 ML experts to review and verify the completeness and appropriateness of the preliminary technical risk factors. A hierarchy of five technical risk categories with 22 technical risk factors was concluded for the analytic hierarchy process (AHP). Then, three rounds of online AHP questionnaires were administered. The consistency ratio (CR) was used to check the respondents’ answers, and the quartile deviation (QD) was applied to assess the consensus on all 96 questions. Finally, we prioritized the technical risk categories and associated technical risk factors. ResultsWe found that "data availability and quality" ranked as the top technical risk category in terms of severity, probability, and impact rankings of the five technical risk categories. Furthermore, all four technical risk factors within this category also occupied the top four positions of impact ranking. ConclusionThe research results highlight the crucial role of the four data availability and quality risk factors for the failure of ML-based software project development. The proposed technical risk model of ML-based software project development with the identified severity and probability priorities may provide practitioners and research community with a clear overview, highlighting areas demanding priority attention to effectively mitigate project failure risks. These findings have broader implications for improving the success rates of ML-based software projects across various domains.

Studying logging practice in machine learning-based applications

Context:Logging is a common practice in traditional software development. There have been multiple studies on the characteristics of logging in traditional software systems such as C/C++, Java, and Android applications. However, logging practices in Machine Learning-based (ML-based) applications are still not well understood. The size and complexity of data and models used in ML-based applications present unique challenges for logging. Objective:In this paper, we aim to bridge this knowledge gap and provide insight into the logging practices in ML-based applications, making the first attempt to characterize current logging practices within a large number of open-source ML-based applications. Method:We conducted an empirical study on 502 open-source ML applications to understand their logging practices, combining quantitative and qualitative analyses and a survey involving 31 practitioners. Results:Our quantitative analysis reveals that logging in ML applications is less common than in traditional software, with info and warn log levels being popular. Top ML-specific logging libraries include MLflow, Tensorboard, Neptune, and W&B. Qualitatively, logging is used for data and model management, especially in model training. Our survey reinforces the importance of logging in experiment tracking, complementing our qualitative findings. Conclusion:Our research carries significant implications. It reveals distinctive ML logging practices compared to traditional software. We have highlighted the prevalence of general-purpose logging libraries in ML code, indicating a potential gap in awareness regarding ML-specific logging tools. This insight benefits researchers and developers aiming to enhance ML project reproducibility and sets the stage for exploring ML-specific logging tools’ impact on machine learning system quality and trustworthiness.