Evaluating the impact of Devsecops on software quality: A systematic review and empirical study

The rapid pace of software development and deployment in today's digital world demands an integrated approach to security, particularly in Continuous Integration/Continuous Deployment (CI/CD) pipelines. Traditional approaches to security, where it is added at the end of the development lifecycle, have proven inadequate in addressing the complexities and vulnerabilities introduced by fast-moving development cycles. DevSecOps (Development, Security, and Operations) represents an evolution of the DevOps culture that integrates security as an essential component throughout the development process, rather than as an afterthought. This paper explores the role of DevSecOps in ensuring continuous security integration within CI/CD pipelines, focusing on its principles, practices, and impact on both security and development efficiency. DevSecOps emphasizes the shift-left approach, where security is integrated early in the software development lifecycle (SDLC) to identify and mitigate vulnerabilities before they reach production. By embedding security practices into CI/CD workflows, organizations can achieve continuous security validation and testing in tandem with software development activities. Key components of a successful DevSecOps implementation include automated security testing, real-time vulnerability scanning, infrastructure as code (IaC) security, and threat intelligence integration. Through the use of tools like static application security testing (SAST), dynamic application security testing (DAST), and software composition analysis (SCA), DevSecOps facilitates the continuous monitoring of code, configurations, and infrastructure for security issues. This paper also examines the cultural shift required to adopt DevSecOps within development teams. It highlights the importance of fostering collaboration between development, security, and operations teams to ensure seamless integration of security practices into the CI/CD pipeline. By aligning security teams with development and operations, DevSecOps fosters a culture of shared responsibility for security, breaking down silos that traditionally hinder effective security integration.

The rapid adoption of DevOps practices has transformed the software development landscape by emphasizing continuous integration, continuous delivery (CI/CD), and agile methodologies. However, this rapid pace of development often introduces significant security challenges, as traditional security testing methods struggle to keep up with the accelerated release cycles. To address these challenges, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into automated security testing has emerged as a promising solution. This paper explores the use of AI and ML to enhance automated security testing within DevOps environments, offering a comprehensive approach to identifying, predicting, and mitigating security vulnerabilities in real time. Automated security testing leverages AI and ML algorithms to analyze code, detect anomalies, and predict potential security threats. These technologies enable the continuous monitoring of codebases, allowing for the early identification of vulnerabilities before they are exploited. By incorporating AI-driven security testing into the CI/CD pipeline, organizations can ensure that security is not an afterthought but a continuous process integrated into every stage of the software development lifecycle. AI and ML models can be trained to recognize patterns associated with security risks, such as code injection, unauthorized access, and data leakage. These models continuously learn from new data, improving their accuracy over time and adapting to evolving threats. The dynamic nature of AI-driven security testing makes it particularly suited for DevOps environments, where frequent code changes and updates can introduce new vulnerabilities. Moreover, AI and ML can assist in automating complex tasks, such as threat modeling, risk assessment, and the prioritization of security issues, enabling security teams to focus on higher-order tasks that require human expertise.

Continuous Integration (CI) is a powerful tool to leverage software development in a safe, reliable, and efficient way. However, in day-to-day of software development, bad habits can arise in working with CI, which can make CI not reach its full potential in projects. These bad habits we call CI bad practices. This paper presented an exploratory study on closed-source projects to investigate how CI bad practices can affect software quality. We observe (1) the impact on the quality internal attributes after the implantation of CI, (2) the evolution of software quality indicators over time, and (3) the bad practices considered most harmful to the software quality by the development teams. Our results mean that projects affected by CI bad practices will not necessarily have their quality impaired. However, this does not mean that quality will be maintained or improved over time. Our analysis of the quality indicators has allowed us to observe that, generally, they have remained stable, and the number of quality issues reported has not decreased considerably either. Finally, the quality indicator most affected by the CI bad practices was maintainability, followed by reliability and security. In addition, the vast majority of the bad practices analyzed were classified as having a medium level of effort for resolution. In this sense, we also classify bad practices according to the level of effort/impact on the quality that can help prioritize their resolution.

Cybersecurity risks are increasing in frequency and complexity, but many organizations struggle to plan and implement adequate protections at all stages of the software development life cycle (SDLC). Security is frequently added at the end of development (afterthought), and making effective use of safeguard space is difficult for IT leaders. The purpose of this study is to produce an all‐encompassing framework to adopt and ensure security throughout each phase of the SDLC, from planning through maintenance. The aim is to minimize vulnerabilities and improve the resilience of software by making “security by design” a structure that not only adopts security elegantly as a living document but also is built to be part of the development process. This study adopted a mixed‐methods approach. The initial stage of inquiry involved a systematic literature review (SLR) to identify common cybersecurity issues associated with each SDLC phase. The SLR was followed by an empirical survey of 71 software professionals from a variety of organizations. The survey was designed to gather perceived threats, current practices, and challenges associated with software development for survey participants' organizations. The data collected were analyzed and reviewed statistically, through chi‐square tests and ANOVA, to profile the variance relative to the size of the organization, geographic region, and experience level of the practitioner. The results noted several high‐risk challenges across the SDLC: underfunded security controls, imprecise requirements, insecure architecture, software bugs (i.e., injection vulnerabilities), inadequate testing, misconfigured production environments, and unreliable maintenance. The proposed framework provides cybersecurity mitigation techniques for each stage of the SDLC, such as leveraging security‐oriented design patterns, secure coding policies (i.e., input validation and authentication protocols), robust testing (i.e., penetration testing and code review), and continuous monitoring after deployment. The implementation of these measures leads to a significant risk reduction in the overall organizational security posture. The framework is a formalized end‐to‐end approach to secure software development by embedding security throughout the cycle. Embedding security as a part of the process versus an afterthought at every stage of the cycle creates a risk reduction impact. This integrated approach also provides organizations with the opportunity to foresee and mitigate events earlier in the cycle, along with general compliance mandates (i.e., GDPR, HIPAA, and PCI‐DSS), to provide more resilient, trustworthy software systems.

Software development depends on various factors and organizational culture is one of them. Recently agile software development, with its emphasis on agility, has received much attention. Research so far has focused on the constructs that define agility and also identified the reasons that limit teams from exhibiting all the constructs of agile software development. This lead to re-defining agility by using economic, quality, simplicity and the relationship with the environment it operates on. One of the factors that contribute to relationship with environment is organizational culture. Though relationship of organizational culture and software methods is not new, analyzing the relationship further in the new defined definition of agility can provide deep insights into software development. In this paper a qualitative research method of multiple case-studies consisting of 3 releases of one product development team was selected. Data was collected using participant-observation method. Research findings have identified some useful insights that could provide researchers and practitioners to develop software development life-cycle that incorporates existing agile methods and uses diverse set of methods to resolve issues including organizational issues. Thereby the new software development life-cycle would not distinguish between agile and plan-driven development methods and could shape itself in the changing context.

Software is a complex thing. It is not an engineering artifact that springs forth from a design by simply following software coding rules; creativity and the human element are at the heart of the process. Software development is part science, part art, and part craft. Design, architecture, and coding are equally important activities and in each of these activities, errors may be introduced that lead to security vulnerabilities. Therefore, inevitably, errors enter into the code. Some of these errors are discovered during testing; however, some are not. The best way to find security errors, whether they are introduced as part of the architecture development effort or coding effort, is to automate the security testing process to the maximum extent possible and add this class of tools to the tools available, which aids in the compilation process, testing, test analysis, and software distribution. Recent technological advances, improvements in computer-generated forces (CGFs), and results in research in information assurance and software protection indicate that we can build a semi-intelligent software security testing tool. However, before we can undertake the security testing automation effort, we must understand the scope of the required testing, the security failures that need to be uncovered during testing, and the characteristics of the failures. Therefore, we undertook the research reported in the paper, which is the development of a taxonomy and a discussion of software attacks generated from the point of view of the security tester with the goal of using the taxonomy to guide the development of the knowledge base for the automated security testing tool. The representation for attacks and threat cases yielded by this research captures the strategies, tactics, and other considerations that come into play during the planning and execution of attacks upon application software. The paper is organized as follows. Section one contains an introduction to our research and a discussion of the motivation for our work. Section two contains a presents our taxonomy of software attacks and a discussion of the strategies employed and general weaknesses exploited for each attack. Section three contains a summary and suggestions for further research.

The Square Kilometre Array (SKA) Observatory is gearing up the formal construction of its two radio interferometers in Australia and South Africa after the end of design and pre-construction phases. Agile methodologies, the Cloud native Computing technologies and the DevOps software ideas are influencing the design of compute infrastructures that will be key to reduce the operational costs of SKA while improving the control and monitoring of the SKA antennas and ancillary systems, Correlators, HPC facilities or related data centre tiered systems. These tools will likely include advanced power metering technologies and efficient distribution automation and Network Operation Centres (NOC). SKA will become the world's largest radio telescope and is expected to achieve its first science by 2026. To cope with this dimension and complexity, a key part of this distributed Observatory is the overall software control and monitoring system embodied in the Observatory Management and Control (OMC) and the Services Teams that requires specialized Agile Teams to assist in software and cyber infrastructure building using an Agile development environment that includes test automation, Continuous Integration, and Continuous Deployment. To manage such a large and distributed machine, the Agile approach was adopted for the core software package of the SKA Telescope aimed at scheduling observations, controlling their execution, monitoring the telescope status and ensuring scalability and reliability. Here, we report on the ENGAGE SKA ciberinfrastructure prototyping support to the SKA Agile Software Development Life Cycle (SDLC).

Small and Medium-sized Enterprises (SMEs) face unique challenges in software deployment, often dealing with limited resources, budget constraints, and the need for fast, reliable updates. This review outlines a Continuous Integration and Continuous Deployment (CI/CD) model specifically tailored to optimize software deployment in SME environments. The proposed CI/CD framework enhances operational efficiency by automating the integration, testing, and deployment processes, significantly reducing the risk of human error and deployment failures. The model leverages key CI/CD practices, including automated testing, version control, and continuous monitoring, to ensure seamless updates and minimal downtime. By integrating tools like Jenkins, GitLab, and Docker, SMEs can streamline code integration, enhance collaboration between development and operations teams, and enable faster, more frequent releases. Automated testing ensures that code changes are validated before deployment, identifying and addressing potential issues early in the development cycle. The framework also emphasizes rollback capabilities and monitoring systems to further reduce deployment risks. Rollbacks allow for quick recovery in case of failure, while continuous monitoring enables real-time detection of performance issues, helping SMEs maintain operational uptime and minimize service disruptions. Moreover, the model supports a microservices architecture, allowing SMEs to scale their applications efficiently without compromising stability. Incorporating CI/CD practices fosters agility, enabling SMEs to respond rapidly to market demands and customer feedback. This model not only improves software quality and deployment speed but also aligns with the budgetary and operational limitations of SMEs. It provides a cost-effective solution that enhances overall system reliability and reduces the frequency of software-related incidents. This CI/CD model serves as a practical approach to software deployment, empowering SMEs to optimize their software development lifecycle while ensuring continuous improvement and operational stability.

Successful implementations of Software Development Methodologies significantly improve software efficiency, collaboration and security. Most companies are moving away from traditional development methodologies towards DevOps for faster and better software delivery. DevOps, which is a primary need of the IT industry, brings development and operation teams together to overcome communication gaps responsible for software failures. It relies on different sets of automation tools to robotize the tasks of software development from continuous integration, to testing, delivery, and deployment. The existence of several automation tools in each development phase raises the need for an integrated set of tools to reduce development time. For this purpose, we used the DevOps-based hybrid model Integrated Tool Chain (ITC), along with three sample java-based projects or code repositories to quantify the results. This paper evaluates and compares measurement metrics of java projects using traditional development methodologies and DevOps, and the results are shown in tabular and graphical format. The latest Google and Stack Overflow Trends have also been included to retrieve the best performer development methodology. This comparative and evaluative performance analysis will be beneficial to young researchers that study the metrics of software development, while also they will be introduced to the automotive environment of DevOps, the latest emerging buzzword in software development.

Modern software development approaches rely extensively on tools. Motivated by practices such as continuous integration, deployment and delivery, these tools are used in a fashion where data are automatically accumulated in different databases as a side-effect of everyday development activities. In this paper we introduce an approach for software engineering data visualization as a mash up that combines data from issue management, software development and production use. The visualization can show to all stake holders how well continuous delivery is realized in the project. The visualization clearly shows the time spent to specify and develop the features as well the length of the delivery cycle. Further more the visualization shows how much work is unfinished and waiting for delivery. This can help the development team to decrease the amount of unfinished work and by that help them to keep up in continuous delivery mind set. In addition to development data usage of the features is also visualized.

Within the dynamic domain of software development, achieving excellence relies heavily on the proficient handling of defects and a comprehensive analysis of their root causes. This abstract thoroughly explores the fundamental roles that defect management and root cause analysis play within the realm of software quality engineering. Defect management involves a methodical approach to identifying, monitoring, and resolving flaws that arise during the software development process. On the other hand, root cause analysis delves into the underlying catalysts behind these defects, with the goal of identifying the core reasons for their occurrence. These abstract underscores the remarkable importance of these practices in the pursuit of superior software quality. Through the implementation of robust defect management strategies and rigorous root cause analysis techniques, the field of software quality engineering aims to mitigate risks, amplify customer satisfaction, and continually refine software development procedures. The exploration of these methodologies forms the bedrock for elevating software quality to unprecedented levels of distinction. "The Significance of Defect Management and Root Cause Analysis in Software Quality Engineering" In the intricate realm of software development, two essential pillars, namely defect management and root cause analysis, emerge as foundational elements of effective software quality engineering. Defects, spanning from inconspicuous errors to glaring faults, hold the potential to undermine the reliability and integrity of software systems. Defect management orchestrates a systematic process encompassing identification, categorization, prioritization, and rectification of these imperfections throughout the software development lifecycle. Concurrently, root cause analysis delves profoundly into the origins of defects, uncovering the underlying triggers that give rise to these issues.Collectively, these practices assume pivotal roles in upholding and enriching software quality.

Technological advancements have contributed to more complex software demands. The Agile approach to software development is widely practiced by the software development industry as it offers faster production with a promise of better software quality. It also provides a flexible process to accommodate changes during the software development life cycle, as per dynamic user requirements. In this paper we characterize the significance of software development models and the role of Agile methodologies in today's dynamic world of technologies. The purpose of this paper is to empirically investigate the choice between two of the Agile methodologies, Scrum and Extreme Programming. The outcome of the investigation based on secondary sources and limited primary sources, reveals that current software industry practices tend to opt for Scrum-based development. This work highlights the benefits of bringing the cloud and Agile methods of software development together, to fully realize the potential of the distributed paradigm.

As enterprises increasingly adopt Azure DevOps for continuous integration and continuous deployment (CI/CD), ensuring robust security and compliance in cloud-based DevOps pipelines has become a critical challenge. This article explores the complex landscape of security and compliance within Azure DevOps, focusing on the hurdles faced by organizations during deployment and operation at scale. We delve into common security risks, such as unauthorized access, data breaches, and configuration vulnerabilities, while also addressing the complexities of adhering to industry-specific compliance frameworks, including GDPR, HIPAA, and SOC 2. The study reviews current best practices for integrating security into the DevOps pipeline, emphasizing the importance of Infrastructure as Code (IaC), identity and access management (IAM), automated security testing, and monitoring tools. Additionally, the article evaluates how Azure DevOps tools, such as Azure Security Center, Azure Policy, and Azure Blueprints, can help streamline compliance management and mitigate risks. Through real-world case studies and expert interviews, this article provides practical solutions for enterprises seeking to enhance their security posture while ensuring compliance within their Azure DevOps workflows. The findings highlight the importance of automated security checks, compliance automation, and the need for a culture of security across DevOps teams to address the challenges of scaling enterprise deployments securely and efficiently.

Purpose - The purpose of this paper is to help in providing insight on the effectiveness in using continuous integration in software development. This study aim is being completed using the help of existing journal and article, theories and literature review of effectiveness implementing the continuous integration.
 Design Approach - A total of 30 Journal and Article including literature review is been analysed using state of the art literature review methodology.
 Findings – Due to limited resources and cost, the review is limited in Software development but provides avenues for future research in difference sector such as industries that required automation in their work. We also found out that the continuous delivery became popular in the current year as there a lot of software and application rises due to the booming of the smartphone technology.
 Problems Statement – Even with the growth of technology some organisation still depends to the traditional method. This is because lack of expertise and no budgets. Continuous integration already exists in over a decade, but the implementation is not widely being introduce.

The DevOps movement intends to improve communication, collaboration, and integration between software developers (Dev) and IT operations professionals (Ops). Automation of software quality assurance is key to DevOps success. We present how automated performance benchmarks may be included into continuous integration. As an example, we report on regression benchmarks for application monitoring frameworks and illustrate the inclusion of automated benchmarks into continuous integration setups. This dataset supplements the paper and contains the raw experimental data for each experiment.