Software Development Practices Research Articles

A Process Model for AI‐Enabled Software Development: A Synthesis From Validation Studies in White Literature

ABSTRACTContextWith the fast advancement of techniques in artificial intelligence (AI) and of the target infrastructures in the last decades, AI software is becoming an undeniable part of software system projects. As in most cases in history, however, development methods and guides follow the advancements in technology with phase differences.PurposeWith an aim to elicit and integrate available evidence from AI software development practices into a process model, this study synthesizes the contributions of the validation studies reported in scientific literature.MethodWe applied a systematic literature review to retrieve, select, and analyze the primary studies. After a comprehensive and rigorous search and scoping review, we identified 82 studies that make various contributions in relation to AI software development practices. To increase the effectiveness of the synthesis and the usefulness of the outcome, for detailed analysis, we selected 14 primary studies (out of 82) that empirically validated their contributions.ResultsWe carefully reviewed the selected studies that validate proposals on approaches/models, methods/techniques, tasks/phases, lessons learned/best practices, or workflows. We mapped the steps/activities in these proposals with the knowledge areas in SWEBOK, and using the evidence in this mapping and the primary studies, we synthesized a process model that integrates activities, artifacts, and roles for AI‐enabled software system development.ConclusionTo the best of our knowledge, this is the first study that proposes such a process model by eliciting and gathering the contributions of the validation studies in a bottom‐up manner. We expect that the output of this synthesis will be input for further research to validate or improve the process model.

Transforming Software Development Through Generative AI : A Systematic Analysis of Automated Development Practices

This article presents a comprehensive analysis of the transformative impact of Generative Artificial Intelligence (GenAI) on software development practices. Through systematic evaluation of implementations across multiple development environments, this article demonstrates how GenAI technologies significantly enhance four critical areas of the software development lifecycle: code generation, automated testing, intelligent code review, and predictive failure analysis. The findings indicate a 45% reduction in initial code development time through automated code generation, 60% improvement in test coverage through AI-driven test case creation, and 30% decrease in post-deployment issues through predictive failure analysis. The article employs a mixed-methods approach, combining quantitative analysis of development metrics from 50 software projects with qualitative assessments from 200 professional developers across diverse organizational contexts. Results reveal that organizations implementing GenAI-assisted development workflows experience a mean productivity increase of 37% while maintaining or improving code quality metrics. Additionally, the article identifies key integration challenges and provides a framework for effective GenAI adoption in software development environments. These findings contribute to the growing body of knowledge on AI-assisted software development and offer practical insights for organizations seeking to optimize their development processes through GenAI integration.

AI Copilot for the Modern Developer : Leveraging GenAI in Software Development

This paper explores the transformative impact of AI copilots on software development, powered by Generative AI and Large Language Models. It examines how these tools enhance developer productivity, improve code quality, and democratize software development. The study delves into the core technologies behind AI copilots, including code synthesis and contextual learning, and discusses their integration with development environments. The paper also investigates the role of reinforcement learning in continuously improving AI copilots and analyzes their impact on software development practices. Additionally, it explores future prospects, including more sophisticated natural language understanding and improved cross-language support. Throughout, the paper emphasizes how AI copilots are reshaping the landscape of software engineering, potentially leading to more efficient, higher-quality, and innovative software solutions.

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.

Improving bioinformatics software quality through teamwork.

Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers' work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians. Additional information and guidance on how to get started is available at https://ferenckata.github.io/ImprovingSoftwareTogether.github.io/.

A survey on Cryptoagility and Agile Practices in the light of quantum resistance

Context:Crypto-agility, a name that stems from agile methodologies for software development, means the ability to modify quickly and securely cryptographic algorithms in the event of a compromise. The advent of quantum computing poses existential threats to current cryptography, having the power to breach current cryptography systems. Objective:We investigated whether and to what extent agile practices for software development are suited to support crypto-agility, or not. In particular, we discuss their usefulness in the context of substituting current algorithms with quantum-resistant ones. Method:First, we analyzed the literature to define a subset of 15 agile practices potentially relevant to cryptographic software development. Then, we developed a questionnaire to assess the suitability of agile practices for obtaining crypto-agility. We performed a Web search of relevant documents about crypto-agility and quantum resistance and sent their authors the questionnaire. We also sent the questionnaire to cybersecurity officers of four Italian firms. We analyzed and discussed the responses to 32 valid questionnaires. Results:The respondents’ affiliations are evenly distributed between researchers and developers. Most of them are active, or somehow active, in quantum-resistant cryptography and use agile methods. Most of the agile practices are deemed to be quite useful, or very useful to get crypto-agility, the most effective being Continuous Integration and Coding Standards; the least appreciated is Self-organizing Team. Conclusion:According to researchers and developers working in the field, the safe transition of cryptographic algorithms to quantum-resistant ones can benefit from the adoption of many agile practices. Further software engineering research is needed to integrate agile practices in more formal cryptographic software development processes.

Exploration of UCD Practice Limitations

Abstract The integration of user-centred design (UCD) practices in software development is fundamental for enhancing the overall experiences of software users. In this paper, we summarize the results of various research studies on what the stated limitations of UCD practices are. A process called the UCD Sprint was introduced recently with the goal of addressing some of the existing UCD practice limitations for integrating UCD practices in software development. The guided usage of the step-by-step process has been studied with students and UCD practitioners in courses showing positive results, where participants describe benefits while using the process, such as the speed of the process, its clear structure, guidance. In this paper, we describe results from a synthesis study summarizing results on how guided usage of the UCD Sprint can address UCD practice limitations. Furthermore, we describe results from a series of interviews with UCD practitioners on how they currently practice UCD, the challenges and limitations they experience and their views on how unguided usage of the UCD Sprint would potentially fit their UCD practices. While the interviewees recognize the importance of user involvement, they highlight challenges in aligning user interests with other stakeholders' interests. All participants had used processes like design sprints for structuring their UCD practices. Interviewees have generally tailored the traditional approach of design sprints, showcasing a nuanced understanding of UCD practices. The results show that the UCD Sprint process is considered by UCD practitioners as a promising approach for practicing UCD. The process is viewed with enthusiasm, but the need for clear objectives and adaptability to the work contexts of the UCD practitioners remains essential. UCD practitioners’ adaptive strategies of their UCD practices emphasize the need for flexibility and adaptation of UCD practices and processes.

Exploring Automated Assertion Generation via Large Language Models

Unit testing aims to validate the correctness of software system units and has become an essential practice in software development and maintenance. However, it is incredibly time-consuming and labor-intensive for testing experts to write unit test cases manually, including test inputs ( i.e., prefixes) and test oracles ( i.e., assertions). Very recently, some techniques have been proposed to apply Large Language Models (LLMs) to generate unit assertions and have proven the potential in reducing manual testing efforts. However, there has been no systematic comparison of the effectiveness of these LLMs, and their pros and cons remain unexplored. To bridge this gap, we perform the first extensive study on applying various LLMs to automated assertion generation. The experimental results on two independent datasets show that studied LLMs outperform six state-of-the-art techniques with a prediction accuracy of 51.82% \(\sim\) 58.71% and 38.72% \(\sim\) 48.19%. The improvements achieve 29.60% and 12.47% on average. Besides, as a representative LLM, CodeT5 consistently outperforms all studied LLMs and all baselines on both datasets, with an average improvement of 13.85% and 26.64%, respectively. We also explore the performance of generated assertions in detecting real-world bugs, and find LLMs are able to detect 32 bugs from Defects4J on average, with an improvement of 52.38% against the most recent approach EditAS . Inspired by the findings, we construct a simplistic retrieval-and-repair-enhanced LLM-based approach by transforming the assertion generation problem into a program repair task for retrieved similar assertions. Surprisingly, such a simplistic approach can further improve the prediction accuracy of LLMs by 9.40% on average, leading to new records on both datasets. Besides, we provide additional discussions from different aspects ( e.g., the impact of assertion types and test lengths) to illustrate the capacity and limitations of LLM-based approaches. Finally, we further pinpoint various practical guidelines ( e.g., the improvement of multiple candidate assertions) for advanced LLM-based assertion generation in the near future. Overall, our work underscores the promising future of adopting off-the-shelf LLMs to generate accurate and meaningful assertions in real-world test cases and reduce the manual efforts of unit testing experts in practical scenarios.

Demystifying The Object-Oriented Features Of Popular Object-Oriented Programming Languages.

Background: Indeed, Object-Oriented Programming (OOP) has emerged as one of the major fundamental paradigms in today’s world of software development as it contains principles that enhance modularity, reusability, and maintainability of software. This article explores deeper into some of the facets of OOP aspects as exhibited in some of the most frequently used programming languages ranging from Java, Python, C++, and C# with an intent of offering substantive information to aspects like encapsulation, inheritance, polymorphism, and abstraction. The article synthesizes the ones focusing on the relevance and conceptualization of OOP and problems related to it in the context of learners and practitioners. It also describes strengths and weaknesses of using C++, Python, and Java in relation to OOP while stressing peculiarity of these languages. Some of the core principles of OOP are highlighted such as encapsulation, inheritance, polymorphism, and abstraction and how they are complicated and significant in today’s software engineering. Materials and Methods: There was a presentation of a survey on which the research is based to determine the problems that programmers encounter when applying OOP features using a sample of 80 programmers in the computer science fields in some Nigerian Universities. Results: Findings reveal the significance of understanding OOP concepts in popular languages and suggest ways of increasing developers’ efficiency, minimizing errors, and improving the quality of developed software. Conclusion: Suggestions include a necessity for training and education to enhance software developers' understanding of OOP features. The findings can guide the creation of training materials and courses centered on OOP concepts, aiming to improve software development practices and elevate the quality of software products.

Agile Methodologies In The Automotive Industry: A Comprehensive Survey

The automotive industry is undergoing a major transformation driven by trends such as autonomous driving, connectivity, electrification, and rapid digitalization. This results in embracing agile software development practices and new digital architectures to accelerate development cycles and manage increasing complexity. This paper discusses the insights on the agile methodologies into automotive software procedures, extending agility beyond software units , integrating agile with product lines, addressing security concerns, and constructing advanced digital platforms for automotive systems also assessment models like ASPLA (Agile Software Product Line Adoption) for agile adoption in software product lines, and future research directions towards realizing the vision of intelligent, self-driving, connected vehicles through continuous evolutions in agile-based development processes, digital architectures complying with quality standards, cross-domain collaborations, and empirical validations

CIPerf: a benchmark for continuous integration services performance and cost analysis

Continuous Integration is a crucial practice in modern software development, enabling teams to automate the process of building, testing, and merging code increments to ensure continuous delivery of high-quality software. Despite its growing adoption, the cost and performance of Continuous Integration services often go unexamined in sufficient detail. This paper presents CIPerf, a comprehensive benchmark designed to analyze both the performance and cost of cloud-based and self-hosted Continuous Integration services. The study centers on a comparison between two specific services: Bitbucket Pipelines, a cloud-based offering by Atlassian, and Hetzner, a self-hosted solution. By focusing on these platforms, the research aims to provide practical insights into the real-world costs and execution performance of Continuous Integration services. To achieve this, CIPerf conducted automated tests on an hourly basis over a two-month period, measuring critical timeframes such as resource provisioning, environment setup, and the actual test execution times. The results showed significant differences in both the cost efficiency and the consistency of performance between the two services. For instance, Bitbucket Pipelines, while convenient in its cloud-based offering, demonstrated higher variability in provisioning times compared to the stable, predictable performance of Hetzner’s self-hosted environment. The analysis also explored how these performance metrics influence key software development metrics, including deployment frequency and developer productivity. CIPerf provides a clear methodology for developers and organizations to objectively assess their Continuous Integration service options, ultimately helping them optimize their workflows. Moreover, this benchmark can serve as an ongoing tool to monitor service performance over time, identifying potential degradations or improvements in service quality, thus offering longterm value for teams that rely on Continuous Integration for their development processes.

Transforming Hydrology Python Packages into Web Application Programming Interfaces: A Comprehensive Workflow Using Modern Web Technologies

The accessibility and deployment of complex hydrological models remain significant challenges in water resource management and research. This study presents a comprehensive workflow for converting Python-based hydrological models into web APIs, addressing the need for more accessible and interoperable modeling tools. The workflow leverages modern web technologies and containerization to streamline the deployment process. The workflow was applied to three distinct models: a GRACE downscaling model, a synthetic time series generator, and a MODFLOW groundwater model. The implementation process for each model was completed in approximately 15 min with a reliable internet connection, demonstrating the efficiency of the approach. The resulting APIs provide standardized interfaces for model execution, progress tracking, and result retrieval, facilitating integration with various applications. This workflow significantly reduces barriers to model deployment and usage, potentially broadening the user base for sophisticated hydrological tools. The approach aligns hydrological modeling with contemporary software development practices, opening new avenues for collaboration and innovation. While challenges such as performance scaling and security considerations remain, this work provides a blueprint for making complex hydrological models more accessible and operational, paving the way for enhanced research and practical applications in hydrology.

Good software development practices improve citations of RNA seq tools

Good software development practices improve citations of RNA seq tools

Comprehensive review of smart city cybersecurity strategies

With cities rapidly advancing technologically, the rise of smart cities brings innovative solutions to urban challenges. However, this integration of technologies within urban infrastructures introduces new cybersecurity risks. This review explores the cybersecurity issues that smart cities face and discusses strategies to address these threats. Smart cities rely on interconnected networks of devices and systems to enhance efficiency, sustainability, and citizen services. Yet, this connectivity also creates a complex environment that is vulnerable to cyberattacks. A key concern is the wide array of IoT devices deployed across smart city infrastructures, which often lack robust security mechanisms, making them susceptible to exploitation by cybercriminals. The interconnected nature of these systems heightens the potential impact of cyberattacks, posing significant risks to critical infrastructure, public safety, and privacy. Threat actors can exploit vulnerabilities in interconnected systems, disrupting essential services, manipulating data, or even causing physical harm. Since smart cities heavily depend on data-driven decision-making, ensuring data integrity and confidentiality is a top priority. To mitigate these risks, various cybersecurity strategies have been proposed and implemented. These include technical solutions, regulatory frameworks, and collaboration among stakeholders. Technical measures like encryption, authentication mechanisms, intrusion detection systems, and secure software development practices are essential. Additionally, strong access controls and network segmentation can limit the scope of potential attacks. Regulatory initiatives also play a crucial role in improving cybersecurity standards and ensuring compliance among stakeholders. Establishing clear guidelines for data protection, privacy rights, and incident response protocols is vital for safeguarding citizens' interests. Collaborative efforts between government agencies, the private sector, academia, and cybersecurity experts are crucial in sharing information and collectively defending against emerging threats. Securing smart cities requires a comprehensive approach that integrates technical measures, regulatory frameworks, and collaborative efforts to create a resilient and secure urban environment for all citizens. Keywords: Cybersecurity, Smart City, AI, Technology, Security.

A systematic literature review on Agile, Cloud, and DevOps integration: Challenges, benefits

Context:In today’s fast-paced digital landscape, integrating DevOps, cloud, and agile methodologies is crucial for meeting software demands. However, this integration remains under-researched. Objective:This study explores the integration of Agile, Cloud, and DevOps in today’s software development landscape. It aims to analyze the challenges and benefits associated with merging these three approaches, focusing on their impact on software testing and the role of mindset in successful implementation and identifying the most suitable Agile methodologies. Methods:This investigation utilizes a Systematic Literature Review(SLR) to enrich comprehension of this integration in current software development practices. Results:The analysis of 31 articles highlights benefits such as improved collaboration and accelerated development, despite challenges with tool proliferation. Platforms like Jenkins, GitLab, Kubernetes, and Docker show promise in addressing these complexities. Our study examines the advantages and challenges of this integration, focusing on its impact on software testing and the role of mindset in successful implementation and identifying the most suitable Agile methodologies. Conclusion:The integration of Agile, DevOps, and Cloud signifies a vital move towards collaborative, scalable, and automated methods, crucial for swift delivery, enhanced quality, and ongoing competitiveness. This unified approach is fundamental for organizational advancement and innovation in the ever-evolving software development realm. Further research should tackle challenges in merging these methods and delve into their interactions with emerging technologies to refine practices for increased efficiency.

Zero Trust Architecture in DevSecOps: Enhancing Security in Cloud-Native Environments

This comprehensive article explores the implementation of Zero Trust Architecture (ZTA) within DevSecOps workflows, focusing on its application in cloud-native environments. The research examines the fundamental principles of ZTA, contrasting it with traditional perimeter-centric security models, and delves into its relevance in the context of modern software development practices. By investigating key challenges such as perimeter dissolution, dynamic workloads, and identity complexity, the study provides insights into the obstacles organizations face when adopting ZTA. The article presents a detailed analysis of best practices for ZTA implementation, including continuous monitoring, identity-based access controls, microsegmentation strategies, and comprehensive encryption policies. Furthermore, it emphasizes the importance of continuous learning and adaptability in maintaining an effective security posture. Through case studies and examination of real-world scenarios, the research highlights successful ZTA implementations and derives valuable lessons for practitioners. The article also explores future directions, considering the potential impact of AI, machine learning, edge computing, and evolving regulatory landscapes on ZTA. By synthesizing current research and industry practices, this article offers a holistic view of ZTA in DevSecOps, providing practitioners and researchers with actionable insights to enhance security in increasingly complex and distributed cloud-native ecosystems.

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.

Enhancing software development practices with AI insights in high-tech companies

Enhancing software development practices with AI insights in high-tech companies

An Empirical Study on the Correctness and Effort to Integrate Feature Models

Feature model integration is pivotal in software development, particularly in evolving software product lines through new feature accommodations. Despite its significance, the influence of developers’ experience on integration efforts and correctness still needs to be more adequately understood. This study conducted a controlled experiment with 25 participants (18 students and seven professionals) following well-known guidelines to run empirical studies. Each participant addressed ten experimental tasks, encompassing 250 integration scenarios, to explore two research questions. The effort and correctness rate in integrating feature models were quantified, revealing that students exerted higher effort (29.23%) and achieved a higher number of correct integrations (39.53%) than professionals. Notably, this superiority lacked statistical significance. Additionally, this article highlights practical implications and noteworthy challenges for the scientific community, providing valuable insights for software development practices. The findings lay a foundation for future studies, delving into software development tasks where students and professionals may achieve comparable results. Finally, this study marks an initial step towards an ambitious agenda, empirically advancing the feature model integration field.

Security and Privacy Challenges in Software as a Service (SaaS)

This document In the rapidly evolving landscape of cloud computing, Software as a Service (SaaS) has emerged as a dominant model for delivering software applications over the internet. However, this model introduces significant security and privacy challenges. This research paper delves into these issues, highlighting the unique risks associated with SaaS platforms. The study explores common vulnerabilities such as data breaches, unauthorized access, data loss, and identity theft, which are exacerbated by the centralized nature of SaaS solutions where data is stored on remote servers managed by service providers. To address these challenges, the paper underscores the importance of robust security measures, including encryption, access controls, and secure software development practices. Additionally, it emphasizes the necessity for SaaS providers to adopt advanced privacy protection techniques like data anonymization and differential privacy to meet user expectations and comply with stringent legal regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Through a comprehensive review of literature and analysis of current SaaS security practices, the research identifies critical risks and provides strategic recommendations for enhancing security and privacy in SaaS environments. By integrating multiple layers of security throughout the software development lifecycle, enforcing strict access management protocols, and ensuring compliance with regulatory standards, SaaS providers can significantly mitigate risks and safeguard sensitive user data. The findings of this study highlight the imperative for continuous improvement in SaaS security strategies to keep pace with emerging threats and technological advancements. Ultimately, by prioritizing security and privacy, SaaS providers can not only protect their users but also gain a competitive edge in the increasingly crowded market of cloud services.