Successful Software Research Articles

Perceived impact of agile principles: Insights from a survey-based study on agile software development project success

ContextAgile methodology has emerged as a fundamental framework guiding software development projects, emphasizing values and principles for achieving successful project outcomes. Despite the widespread recognition of the importance of agile principles, there remains a gap in empirical research investigating their actual impact on agile project success. ObjectiveThis research aims to examine the relationship between agile principles and project outcomes and provide empirical evidence supporting the importance of agile principles in achieving success in agile software development (ASD) projects. MethodA total of 298 Agile project practitioners participated in an online survey between August and September 2023 to test this study's research model using the partial least square structural equation modeling (PLS-SEM) method. ResultsWe find a significant relationship between adopting agile principles and project success, with regular delivery, technical excellence, team member proactivity, and customer collaboration showing the highest impact on Agile project success. However, process simplicity was found not to be significant in the study. ConclusionsOur analysis verifies the importance of Agile principles and suggests areas for further study to successfully understand their impact on Agile projects. The findings contribute to the ongoing discourse on agile principles and their impact on software development project success, opening avenues for future research and the refinement of agile methodologies. These insights could assist organizations in optimizing Agile practices and decision-making, leading to more successful and efficient software development projects.

Evaluation and Assessment of Software Security Risks and Vulnerabilities Within the Realm of Secure DevOps.

Security plays a vital role in successful software development and consistency, especially in a modern, fast-expanding world dominated by technologies. It guarantees that all privacy, integrity, and accessibility of information, coding, and services are protected. Achieving such requires a consistent consideration of security throughout all developmental stages of software. Although several methods for improving the quality of software have been created, there is still no clear method for effectively integrating security throughout each stage of the software developmental process. The inability to give adequate attention to security has been identified as the main cause of common flaws. Many forward-thinking businesses frequently employ a "patch and infiltrate” technique, taking security into consideration only after the execution of the task. Higher costs, project delays, neglect of verification and integration during the development life cycle of the project, reliance on external tools and parts, and ignorance among all factors contribute to the disregard of security during the SDLC. Nevertheless, Secure-DevOps is indispensable for the survival of companies in the Information and Communication Technology (ICT) landscape. Consequently, applying an effective approach that deals with security within every level of the SDLC is crucial. To bridge this gap, a comprehensive examination of effective techniques for developing secure software was presented, taking into account the financial and time constraints of a project. A framework for ensuring security built upon Secure-DevOps practices, which seamlessly integrates the best security measures into different stages of the SDLC, was identified. To validate this framework, a mathematical model has been employed. The study concludes that effectively incorporating Secure-DevOps practices throughout the Software developmental process, minimizes security risks and vulnerabilities.

A systematic literature review on characteristics of the front-end phase of agile software development projects and their connections to project success

ContextSoftware development of new products and services often involves a front-end phase where user needs are analysed, costs and benefits are estimated, and initial plans are created. GoalThis study aims to learn more about how the introduction of agile software development has affected practices and outcomes related to cost and benefit estimation in this front-end phase and to understand better what would improve this phase. MethodWe identified, reviewed and aggregated the results from 42 relevant research articles by searching literature databases and snowballing relevant articles. ResultsThe front-end phase of agile was found to be, on average, similar and just as comprehensive as that of non-agile software development. This may be unfortunate, given the finding that more successful agile software development is connected with less detail in cost estimation and planning-related activities. A less comprehensive front-end phase may be especially beneficial for low-risk agile software development. ConclusionThe results of this review suggest that agile principles, so far, have had a limited influence on the front-end phase. We recommend more flexibility and context-dependency in how the front-end phase of agile software development is conducted, including less comprehensive estimation and planning activities for low-risk software development contexts.

Holistic personas to increase the novice developer productivity

A deeper understanding and integration with system users' thoughts and emotional experiences are required for user-engaged development. User experience (UX) journey integrates user requirements and problem-solving approaches. The integration of data-driven techniques and user-centric approaches in software development is investigated in this study. It focuses on using the Markov chain model to predict developer productivity based on data gathered while creating personas across three projects. Organizations can gain valuable insights into user needs and requirements by conducting purposeful activities such as strength, weaknesses, opportunities, and threats (SWOT) analysis, competitor analysis, hypothesis formulation, identification of behavioral variables, mapping interviews, and defining characteristics and objectives. The model has predictive capabilities that allow for more informed decision-making, more efficient resource allocation, and better project planning. The goal of the activity and the model ensure the development of software products that effectively meet the needs of users, resulting in a higher success rate for software development initiatives. This study emphasizes the importance of integrating quantitative and qualitative analysis to drive successful software development projects and increase productivity while meeting user needs. According to the findings of the research conducted from the three projects completed, the proposed methods have similarities, and predictions using the Markov chain can determine the success of novice developers.

Remote Controlled Agricultural Robot

Abstract: The Agri Bot project presents a pioneering endeavor in the realm of autonomous agriculture by seamlessly integrating robotics, IoT, and mobile application development. Utilizing an Arduino UNO microcontroller, ESP32 camera module, servo motors, and various sensors, the system excels in executing precise agricultural tasks such as digging, filling, seed sowing, and fertilizer dispensing. The accompanying mobile application, developed through MIT App Inventor and utilizing Bluetooth communication, provides an intuitive interface for users to remotely oversee and manage the Agri Bot's operations. Beyond its successful hardware and software integration, the project holds transformative potential for traditional farming practices, evident in the incorporation of advanced technologies such as obstacle detection sensors and machine learning for crop recognition. The modular design and collaborative potential of swarm robotics hint at scalability and adaptability for evolving agricultural needs. As the project lays the groundwork for autonomous agriculture, future prospects include autonomous navigation, sustainable power solutions, data analytics for decision support, and enhanced remote monitoring. Collaboration with agricultural experts and ongoing refinements based on real-world feedback are crucial for optimal deployment in diverse farming environments. The Agri Bot project represents a significant milestone in the convergence of technology and agriculture, heralding a future where autonomous systems play a central role in shaping the agricultural landscape.

ProgEdu4Web: An automated assessment tool for motivating the learning of web programming course

AbstractThe expanding software industry has increased the demand for proficient programming professionals. However, more than technical expertise is needed for successful software development; code quality assurance and effective teamwork are also indispensable. Unfortunately, recent graduates often exhibit deficiencies in programming, code quality assurance, and teamwork skills, highlighting the need for improvements in these areas. In response to these challenges, this study presents ProgEdu4Web, an innovative Automated Programming Assessment System designed to evaluate code quality and teamwork proficiency in web programming. The system integrates automatic source code evaluation tools for static code analysis, ensuring timely feedback on coding quality issues. To assess the effectiveness of ProgEdu4Web, a questionnaire and hypothesis model was employed, surveying a cohort of 111 students enrolled in web programming classes. The study's findings demonstrate that the system significantly alleviated instructor workload while motivating students to enhance their web programming with good code quality and teamwork abilities.

Ensemble feature ranking approach for software fault prediction

Software fault prediction, which aims to find and fix probable flaws before they appear in real-world settings, is an essential component of software quality assurance. This article provides a thorough analysis of the use of feature ranking algorithms for successful software failure prediction. In order to choose and prioritise the software metrics or qualities most important to fault prediction models, feature ranking approaches are essential. The proposed focus on applying an ensemble feature ranking algorithm to a specific software fault dataset, addressing the challenge posed by the dataset’s high dimensionality. In this extensive study, we examined the effectiveness of multiple machine learning classifiers on six different software projects: jedit, ivy, prop, xerces, tomcat, and poi, utilising feature selection strategies. In order to evaluate classifier performance under two scenarios—one with the top 10 features and another with the top 15 features—our study sought to determine the most relevant features for each project. SVM consistently performed well across the six datasets, achieving noteworthy results like 98.74% accuracy on “jedit” (top 10 features) and 91.88% on “tomcat” (top 10 features). Random Forest achieving 89.20% accuracy on the top 15 features, on “ivy.” In contrast, NB repeatedly recording the lowest accuracy rates, such as 51.58% on “poi” and 50.45% on “xerces” (the top 15 features). These findings highlight SVM and RF as the top performers, whereas NB was consistently the least successful classifier. The findings suggest that the choice of feature ranking algorithm has a substantial impact on the fault prediction models’ predictive accuracy and effectiveness. When using various ranking systems, the research also analyses the trade-offs between computing complexity and forecast accuracy.

Exploring issues of story-based effort estimation in Agile Software Development (ASD)

ContextEffort estimation based on user stories plays a pivotal role in agile software development, where accurate predictions of project efforts are vital for success. While various supervised ML tools attempt to estimate effort, the prevalence of estimation errors presents significant challenges, as evidenced by the CHAOS report by the Standish Group, which highlights incorrect estimations contributing to a substantial percentage of failed agile projects. ObjectivesThis research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy of the user story-based estimation method. MethodsTo achieve the research objectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. The review encompasses diverse estimation methods, user story attributes, and the array of challenges that can result from inaccurate estimations. ResultsThe SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition as crucial determinants of estimation reliability. Consistency in user stories, technical complexities, and task engineering practices also emerge as significant contributors to estimation inaccuracies. The study underscores the interconnectedness of these issues, emphasizing the need for a standardized protocol to minimize inaccuracies and enhance estimation precision. ConclusionIn light of the findings, it becomes evident that addressing the multi-dimensional factors influencing user story-based effort estimation is imperative for successful agile software development. The study underscores the interplay of various aspects, such as team dynamics, task complexity, and requirement engineering, in achieving accurate estimations. By recognizing these challenges and implementing recommended solutions, software development processes can avoid failures and enhance their prospects of success in the agile paradigm.

Empowering Chronic Kidney Disease Patients: A Personalized Electronic Health Record System for Enhanced Care and Management

Introduction: Electronic health records are critical for diagnosis and treatment, but their development can be challenging and time-consuming process. Personal health records provide a practical and accessible solution by empowering patients to take an active role in their care. This study presents a personalized electronic health record system for chronic kidney disease patients, highlighting its potential benefits on patient care.Material and Methods: In 2021, a practical study was conducted to develop and evaluate a personal health records software specifically for patients with chronic kidney disease. The analysis phase involved a literature review and validation process to review the dataset and key software functions. The study primarily focuses on the implementation process and exploratory usability evaluation results of the software. The development and evaluation process involved a health information management specialist and a medical informatics expert, and the software was evaluated by health information technology students using the Nielsen usability evaluation checklist.Results: In the evaluation of the produced software, 12 issues were identified, of which three were related to the principle of user freedom and mastery over the system, three were related to the principle of consistency and standards, one was related to the principle of aiding users in diagnosis, and five were related to error prevention.Conclusion: This study emphasizes the importance of compliance with clinical standards and user experience in developing successful personal health record software. Previous failures in similar systems have been attributed to non-compliance and unmet user expectations. The study aims to identify obstacles and improve the personal health records production process.

TeReKG: A temporal collaborative knowledge graph framework for software team recommendation

Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member’s competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers’ views, and the method ignored the temporal aspect of open-source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state-of-the-art team recommendation algorithms using three popular open-source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state-of-the-art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.

Analysis and Design of Multi-story (G+6) Building by using STAAD Pro

This project is named “Analysis and design of multi-storey building by using STAAD Pro” which involves the analysis of 3D frames of varying floor heights and varying no. of bays using a highly successful software tool STAAD Pro. To compete in the ever-growing competent market, a structural engineer needs to save time as well as Cost. As a sequel to this, an attempt is made to analyze and design a multi-storied building by using the software package STAAD Pro. For analyzing a multi-storied building, one must consider all the possible load(s) and see that the structure is safe against all possible load conditions. There are several methods for the analysis of different frames like Kani's method, Cantilever method, Portal method, and Matrix method. The project deals with the design & analysis of a multi-storied residential building of G+6 consisting of 2 apartments on each floor. The Dead load & Live loads are applied and the design for Beams, Columns & Footing is obtained by utilizing STAAD Pro’s new features which have surpassed its predecessors and compotators like AutoCAD, in data sharing capabilities. We conclude that STAAD Pro is a very powerful tool that can save much time and cost and is very accurate in Designs calculations and it is suitable for the design of a multistoried building.

Phonons from density-functional perturbation theory using the all-electron full-potential linearized augmented plane-wave method FLEUR * *Dedicated to the memory of Henry Krakauer (1947–2023).

Phonons are quantized vibrations of a crystal lattice that play a crucial role in understanding many properties of solids. Density functional theory provides a state-of-the-art computational approach to lattice vibrations from first-principles. We present a successful software implementation for calculating phonons in the harmonic approximation, employing density-functional perturbation theory within the framework of the full-potential linearized augmented plane-wave method as implemented in the electronic structure package FLEUR. The implementation, which involves the Sternheimer equation for the linear response of the wave function, charge density, and potential with respect to infinitesimal atomic displacements, as well as the setup of the dynamical matrix, is presented and the specifics due to the muffin-tin sphere centered linearized augmented plane-wave basis-set and the all-electron nature are discussed. As a test, we calculate the phonon dispersion of several solids including an insulator, a semiconductor as well as several metals. The latter are comprised of magnetic, simple, and transition metals. The results are validated on the basis of phonon dispersions calculated using the finite displacement approach in conjunction with the FLEUR code and the phonopy package, as well as by some experimental results. An excellent agreement is obtained.

Sustainable agility of product development process based on a rough cloud technique: A case study on China's small and medium enterprises.

The importance of incorporating an agile approach into creating sustainable products has been widely discussed. This approach can enhance innovation integration, improve adaptability to changing development circumstances, and increase the efficiency and quality of the product development process. While many agile methods have originated in the software development context and have been formulated based on successful software projects, they often fail due to incorrect procedures and a lack of acceptance, preventing deep integration into the process. Additionally, decision-making for market evaluation is often hindered by unclear and subjective information. Therefore, this study introduces an extended TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method for sustainable product development. This method leverages the benefits of cloud model theory to address randomness and uncertainty (intrapersonal uncertainty) and the advantages of rough set theory to flexibly handle market demand uncertainty without requiring extra information. The study proposes an integrated weighting method that considers both subjective and objective weights to determine comprehensive criteria weights. It also presents a new framework, named Sustainable Agility of Product Development (SAPD), which aims to evaluate criteria for assessing sustainable product development. To validate the effectiveness of this proposed method, a case study is conducted on small and medium enterprises in China. The obtained results show that the company needs to conduct product structure research and development to realize new product functions.

Towards a successful secure software acquisition

Towards a successful secure software acquisition

Development of the SIDEM Application (Advanced Village Information System) as an Administrative Management System for Kedawung Village

PPK Ormawa is a community service and empowerment program initiated by student organizations. In particular, PPK Ormawa HMJ Informatics at the Widya Gama Lumajang Institute of Technology and Business devised SIDEM, a village administration system for Kedawung Village. SIDEM utilizes Agile methods, allowing swift and successful software development. The system's outcome is a user-friendly design benefiting both village officials and communities. The application of Agile methods facilitated flexibility, promptly adapting to evolving requirements and incorporating feedback. This approach ensured the system's functionality while aligning with Kedawung Village's specific needs. Agile's rapid development further enabled timely implementation, potentially significantly improving village administration and communication. SIDEM's design emphasis on usability demonstrates a commitment to user-centered development, empowering local stakeholders. Ultimately, the success of SIDEM's Agile-driven development underscores the significance of agility and user-centric design in community service initiatives. This model serves as a valuable example for future technology-based community empowerment projects.

The key factors contribute to time pressure in software development projects: A review

The success of software development projects is often hindered by time pressure (TP), leading to decreased productivity, compromised quality, and increased risk of failure. To address this issue, it is crucial to understand the key factors contributing to TP in software development projects. In line with the study's objectives, the review methodology followed the Kitchenham and Charters criteria, and a search strategy encompassed four primary digital databases, namely IEEE, ACM Digital Library, Science Direct, and Springers, resulting in 4,500 relevant sources. After applying inclusion and exclusion criteria, a total of 128 papers were selected for analysis. This paper offers a comprehensive overview of the factors contributing to TP in software development. This study synthesizes the findings from multiple studies to guide practitioners in improving their project management approaches and highlights the significance of enhancing various aspects of the development process. The findings highlight the importance of improving project management, estimation techniques, knowledge, and skills to effectively manage TP. Additionally, managing requirements volatility, setting clear goals and objectives, and reducing distractions and interruptions emerge as crucial strategies for mitigating TP and enhancing project success. Furthermore, selecting software developers based on their personality traits is recommended to foster a work environment conducive to reduced TP and improved software development outcomes. By understanding and addressing these factors, software development teams can alleviate TP and increase the likelihood of successful software products. Implementing these recommendations can contribute to reduced TP, improved project outcomes, and enhanced overall success in software development.

MLOps for PHM Systems

Advances in machine learning (ML) techniques allow practitioners to generate substantial predictive value from historical data. Modern sensors generate vast amounts of data which inform prognostic health management (PHM) systems. As ML techniques continue to grow in importance for PHM, the system that manages and deploys ML models becomes critical for successful production software. Machine Learning Operations (MLOps) is centered around implementing continuous integration and deployment (CI/CD) practices in the context of ML applications. We will present MLOps designs for deploying machine learning based PHM software and discuss ML pipelines that automate data ingestion, model training, testing, deployment, and monitoring. The principles we will examine ensure model quality, performance, and software stability. We will call attention to important design considerations and demonstrate solutions for the full model lifecycle when building MLOps pipelines for PHM systems.

Software Testing: A Prediction Techniques

Prediction of software defects using machine learning techniques has attracted more attention from researchers due to its importance in producing successful software. On the other side, it reduces the cost of software development and facilitates procedures to identify the reasons for determining the percentage of defect-prone software in the future. There is no conclusive evidence for specific types of machine learning that will be more efficient and accurate in predicting software defects. However, some of the previous related work proposes ensemble learning techniques as a more accurate alternative. The paper presents the resample technique with three types of ensemble learners; Boosting, Bagging, and Rotation Forest, using eight base learners tested on seven types of benchmark datasets provided in the PROMISE repository. Results indicate that accuracy has been improved using ensemble techniques more than single leaners especially in conjunction with Rotation Forest with the resample technique in most of the algorithms used in the experimental results.

SparkMaster 2: A New Software for Automatic Analysis of Calcium Spark Data.

Calcium (Ca) sparks are elementary units of subcellular Ca release in cardiomyocytes and other cells. Accordingly, Ca spark imaging is an essential tool for understanding the physiology and pathophysiology of Ca handling and is used to identify new drugs targeting Ca-related cellular dysfunction (eg, cardiac arrhythmias). The large volumes of imaging data produced during such experiments require accurate and high-throughput analysis. We developed a new software tool SparkMaster 2 (SM2) for the analysis of Ca sparks imaged by confocal line-scan microscopy, combining high accuracy, flexibility, and user-friendliness. SM2 is distributed as a stand-alone application requiring no installation. It can be controlled using a simple-to-use graphical user interface, or using Python scripting. SM2 is shown to have the following strengths: (1) high accuracy at identifying Ca release events, clearly outperforming previous highly successful software SparkMaster; (2) multiple types of Ca release events can be identified using SM2: Ca sparks, waves, miniwaves, and long sparks; (3) SM2 can accurately split and analyze individual sparks within spark clusters, a capability not handled adequately by prior tools. We demonstrate the practical utility of SM2 in two case studies, investigating how Ca levels affect spontaneous Ca release, and how large-scale release events may promote release refractoriness. SM2 is also useful in atrial and smooth muscle myocytes, across different imaging conditions. SparkMaster 2 is a new, much-improved user-friendly software for accurate high-throughput analysis of line-scan Ca spark imaging data. It is free, easy to use, and provides valuable built-in features to facilitate visualization, analysis, and interpretation of Ca spark data. It should enhance the quality and throughput of Ca spark and wave analysis across cell types, particularly in the study of arrhythmogenic Ca release events in cardiomyocytes.

Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles

As the process of urbanization continues to accelerate, the demand for sustainable cities has become more critical than ever before. The incorporation of electric vehicles (EVs) is a key component in creating sustainable cities. However, the development of smart cities for EVs entails more than just the installation of charging stations. Software engineering plays a crucial role in realizing smart cities for electric vehicles. This paper examines the role of software engineering in the creation of smart cities for electric vehicles, the techniques utilized in electric vehicle charging infrastructure, the obstacles faced by software engineers, and the future of software engineering in sustainable cities. Specifically, the paper explores the significance of software engineering in integrating EVs into the transportation system, including the design of smart charging and energy management systems, and the establishment of intelligent transportation systems. Additionally, the paper offers case studies to demonstrate successful software engineering implementations for smart cities. Finally, the paper concludes with a discussion of the challenges that software engineers encounter in implementing intelligent transportation systems for EVs and provides future directions for software engineering in sustainable cities.