Menu
According to Al-Ameen et al., (2021), Artificial Intelligence (AI) has completely changed the DevOps environment by enhancing the protection and effectiveness of software development procedures. To automate tedious operations, spot possible security issues, and guarantee regular delivery of high-quality software, developers working in DevOps deploy AI-powered tools and approaches. In this response, we'll go through how AI is used in DevOps for safe and continuous delivery as well as how the UTUAT 2 model and qualitative analysis theory may be used in this situation.
The goal of qualitative analysis concept is to comprehend social processes through the study of qualitative data from sources including survey responses, observations, and writing. Exploring complicated aspects that cannot be measured, such user views and occasions, makes use of it especially well. The use of qualitative analysis concept in the framework of AI in DevOps can be utilized to better understand how development and operations teams view the integration of AI into their workflows, spot adoption roadblocks, and find areas for advancement (Al-Ameen et al., 2021).
A methodology for assessing the quality of software is the UTUAT 2 framework (Usability, Testability, Understandability, Accessibility, and Traceability). In regards to convenience, authenticity, comprehension, accessibility, and transparency, it can be utilized to evaluate the efficacy of AI-powered DevOps systems. DevOps teams can make sure that their AI-powered solutions are available to all clients, easy to test, easy to comprehend, and give a clear audit trail by leveraging the UTUAT 2 paradigm.
Camilleri (2022) explained that some specific instances of how DevOps utilizes AI for continuous and secure shipment. Tools that use AI to power testing can evaluate code changes and create test scenarios and test information automatically. This shortens the manual testing process and guarantees that all potential cases are addressed. AI algorithms are capable of analyzing code repositories to find potential security flaws like SQL injection or cross-site scripting. DevOps teams can now address security vulnerabilities proactively before they develop into an issue.
AI can examine data from operational settings to spot patterns and trends that can point to upcoming problems. DevOps teams can proactively fix possible issues as a result before they have an effect on clients.Tools driven by AI can automate the installation procedure, lowering the risk of human mistake and guaranteeing that software is distributed regularly and dependably.
Camilleri (2022) concluded that by enhancing the security, effectiveness, and standard of software development techniques, AI has completely changed the DevOps scene. It is possible to use the UTUAT 2 model and qualitative analysis theory to make sure that all DevOps team members can use and benefit from AI-powered solutions.
Faccia (2023) described that the adoption and application of AI in DevOps can be influenced by a variety of factors. The perceived utility of AI is one of the key elements. If DevOps teams think AI would enhance protection and regular distribution of software applications, they are more inclined to employ it.
The perceived usability of AI is a further consideration. If DevOps teams think AI is simple to use and can be integrated into their current processes, they are more inclined to accept it.
The implementation of AI in DevOps may also be influenced by social factors. If industry pioneers or competitors are implementing AI in DevOps, it might inspire other DevOps teams to follow suit.
The implementation of AI in DevOps can also be influenced by facilitating factors, such as having access to the required tools and assistance.
Adoption can also be influenced by hedonic motivation, or the perception of satisfaction from employing AI in DevOps. DevOps teams may be more willing to accept AI if they perceive using it to be entertaining or fulfilling (Faccia, 2023).
According to Havens (2020), DevOps may employ AI in a variety of ways to enhance software product security and regular delivery. Anomaly detection is one of the main purposes of AI in DevOps. Artificial intelligence (AI) can be programmed to detect trends and actions that deviate from the norm. DevOps teams may benefit from being able to swiftly recognize and address possible security concerns or performance problems.
Predictive analytics is another way that AI is used in DevOps. To find trends and foresee potential problems in upcoming releases, AI may evaluate information from previous versions. This can aid DevOps teams in taking proactive measures to address problems before they affect end users Havens (2020).
Testing that is automated can also be done using AI. Software testing can be performed by AI more quickly and accurately than by human testers. DevOps teams may utilize this to find bugs and other problems before they are made available to final clients.
The discipline of DevOps has seen substantial evolution with the advent of new devices and technologies. Artificial intelligence (AI) is one of the most exciting technologies to gain traction in the past few years. Hoard (2021) explained that, AI has the ability to change DevOps team workflows and enhance software product safety and regular delivery. In this research, we'll examine the application of AI to DevOps and how it may be used to accomplish continuous and secure execution.
AI has the ability to change DevOps team workflows and enhance software product safety and regular delivery. DevOps teams can find and fix problems before they affect end users by utilizing AI for anomaly identification, predictive analytics, and automated testing. Perceived utility, usability, social impact, enabling circumstances, and hedonic motivation are some of the aspects that affect the adoption and use of AI in DevOps. It's crucial for DevOps teams to properly take into account these elements in order to fully utilize the advantages of AI in DevOps (Hoard, 2021).
DevOps (Development Operations) provides many use cases for artificial intelligence (AI). One of the core tenets of DevOps is the execution pipeline, which may be automated using AI. According to Nair et al., (2020) Companies can accelerate their regular delivery procedures and shorten their time to market by utilizing AI algorithms. To identify abnormalities and possible issues before they become a concern, AI might be utilized to track and analyze user behavior, logs, and system efficiency. Companies may proactively discover problems and take measures to fix them before they have an impact on consumers by adopting AI-based surveillance solutions.
AI may be used to evaluate vast volumes of data and forecast upcoming patterns and tendencies. This can assist businesses in making educated choices regarding their DevOps procedures, such as the distribution of resources, release calendaring, and capacity allocation.Code assessments, scrutiny, and deployment are just a few examples of normal jobs and procedures that can be automated using intelligent automation (AI). Employing AI-based automation tools allows businesses to increase productivity by freeing up resources for higher-level work. Teams of developers and operational personnel can receive help and assistance from AI-powered chatbots and virtual assistants. They can aid with troubleshooting, give advice, and provide answers to frequently asked issues, freeing up personnel so they can concentrate on more difficult duties (Nair et al., 2020).
By automating repetitive tasks, seeing patterns, and producing insights that can be utilized to maximize performance, artificial intelligence (AI) has the ability to completely transform the DevOps process. Using AI in DevOps has the following benefits Serra (2021):
Automation of repetitive chores like testing, inspection, and deployment by AI frees up software engineers and operations personnel to concentrate on more strategic initiatives. This may result in quicker timelines for delivery, better software, and lower costs.
AI can spot patterns in past data and anticipate problems before they arise. This can assist units in proactively addressing possible issues, minimizing downtime and raising system availability generally.
AI can examine system efficiency information to pinpoint areas that could be improved, giving operations and development teams useful information. As a result, the procedure for creating software may continue to advance and produce software of higher caliber.
By automating duties that would normally require a lot of labor, AI may assist enterprises expand their DevOps operations. In spite of rising demand, this can help firms provide software more quickly and consistently.
Using AI, the software creation procedure can uncover and fix security flaws. AI can identify dangers and take action faster and more efficiently than workers by constantly seeing and analyzing system activity.
According to Shafique et al., (2021), Artificial Intelligence (AI) has grown in importance as a technology, assisting enterprises in enhancing the security and effectiveness of their software creation and installation processes in the subject of DevOps. The effects of AI on DevOps for safety and constant delivery can be assessed in this setting using the qualitative analysis concept and the UTUAT 2 framework.
Qualitative analysis concept concentrates on comprehending and interpreting how people act and experiences. It entails looking for general trends in data that isn't quantifiable in the environment, such text or images. This method can be used to investigate AI in DevOps by looking at how it is utilized, how the parties view it, and what effects it has on software creation and implementation.
A framework for assessing the efficacy of software testing is the UTUAT 2 model. To make sure that software satisfies user needs, the acronym UTFACT represents Use scenario, Test scenario, Unit assessment, Acceptance assessment, and Traceability. By analyzing how AI might be used to enhance every phase of the testing procedure, the framework can be used to assess the importance of AI in DevOps (Shafique et al., 2021).
Shafique et al., (2021) explained that, we can better grasp how AI is affecting DevOps for safety and constant delivery when these two methodologies are coupled. Examples of how AI is being utilized to automate and improve many components of the DevOps procedure, such as code inspections, testing, and deployment, can be found using qualitative analysis concept. This can assist businesses in determining the most practical applications of AI and ensuring that it is applied in ways consistent with their principles and aims.
The efficiency of AI at each step of the DevOps process may be assessed using the UTUAT 2 paradigm. AI can be used, for instance, to identify and rank scenarios and test instances, automate unit tests, and examine the findings of acceptance assessments. Companies can determine the phases of the testing procedure where AI has the greatest influence and then modify their DevOps procedures to focus on those phases (Shafique et al., 2021).
Overall, the UTUAT 2 model and qualitative analysis theory can offer insightful information on how AI is affecting DevOps for secure and constant delivery. Companies can make sure they are utilizing this potent technology to enhance both the safety and effectiveness of their software development and deployment procedures by employing these methods to assess the application of AI in DevOps.
The last few years have seen a substantial increase in interest in the application of artificial intelligence (AI) in DevOps for safe and continuous delivery. I'll give a qualitative study of the advantages of AI in DevOps and how it helps with continuous and safe distribution in this reply. I'll also describe the features of the UTUAT 2 framework (Wang et al., 2020).
DevOps processes benefit greatly from AI's ability to increase their speed, accuracy, and efficiency. The following are some advantages of applying AI to DevOps:
Improved Automation: Devices and algorithms powered by AI can automate monotonous processes and lessen human involvement. Deployments become quicker and error-free as a consequence.
Predictive Analytics: AI algorithms are able to examine vast amounts of data to find trends and forecast future events. This aids in proactive problem solving and downtime reduction.
Better Security: Real-time anomaly detection is a capability of AI algorithms. This aids in enhancing the overall security of the DevOps procedure.
AI algorithms are capable of intelligent testing by selecting examinations and situations that are essential to the success of the application. This enhances the effectiveness of the application.
A testing methodology known as UTUAT 2 puts an emphasis on User, Transaction, Use-Case, Design, and Test Scenario. The UTUAT 2 model's standards are as follows:
User: The framework places a strong emphasis on the value of knowing the user's needs and desires from the application.
Transaction: According to the concept, a transaction is a series of actions that a user takes to accomplish a specific task.
Use-case: The model underlines how crucial it is to comprehend the application's use-cases.
The application's design is the main focus of the model, along with how it affects testing.
The model highlights the significance of creating thorough test cases that account for all important circumstances.
To make sure that the application is extensively tested and satisfies the user's expectations, the UTUAT 2 framework can be utilized in DevOps operations.
In summary, applying AI to DevOps can improve the process' overall speed, precision, and effectiveness. To make sure that the software is extensively tested and satisfies the user's expectations, the UTUAT 2 framework can be utilized in DevOps operations.
In the discipline of DevOps, artificial intelligence (AI) has grown in significance, especially in connection to defense and constant delivery. The significance of AI in DevOps and how it might be utilized utilizing the UTUAT 2 model parameters are qualitatively analyzed below (Williams, 2022).
Improved Effectiveness: AI systems examine data and spot patterns more quickly than people can. By automating repetitive operations like evaluation, surveillance, and implementation, this can dramatically increase DevOps productivity.
Continuous Delivery: DevOps places a strong emphasis on constant delivery, and AI can be quite helpful in attaining this objective. Companies can use AI to deliver new code more quickly, automate the procedure for testing, and find and solve errors.
Security: One of the most important aspects of DevOps is safety, and AI may help by identifying and reducing security concerns in real-time. AI is also capable of locating weaknesses in the infrastructure and making suggestions for how to fix them.
A testing structure for assessing software quality is the UTUAT 2 model. Unit, Evaluation, Utilization, Audit, and Training are its initials. The details of how AI might be used to improve each part of the UTUAT 2 framework are provided below (Williams, 2022):
In DevOps, AI is crucial, especially in terms of safety and continuous delivery. Companies can use artificial intelligence to boost productivity, increase security, and accomplish continuous delivery by implementing the UTUAT 2 framework specifications.
Software creation and execution procedures can be made faster, more effective, and more secure by incorporating AI into DevOps. In this situation, qualitative research concept can be applied to assess how well AI-based DevOps processes ensure continuous and secure delivery.
According to Havens (2020), the application of the grounded methodology technique is one potential theory for qualitative research. In order to create a theory that is supported by the evidence, this method entails examining data from several sources, including interviews, observations, and documents. The results of this concept can then be applied in real applications or utilized to direct future study.
Data can be gathered from a variety of sources, including software development groups, IT support groups, and security specialists, to apply the Grounded Theory method to the assessment of AI-based DevOps techniques. The information can be examined to find recurring themes and trends in relation to the application of AI in DevOps, as well as its effects on safety and continuous delivery Havens (2020).
Havens (2020) explained that the UTUAT 2 framework may be utilized to establish requirements for AI-based DevOps techniques in addition to qualitative evaluation. Software testing is based on the UTUAT 2 framework, which emphasizes functionality, reliability, upkeep, and versatility. The UTUAT 2 framework can be used to establish standards for the functionality and upkeep of AI tools utilized in DevOps processes in the framework of AI-based DevOps.
For instance, a usability demand would be that AI-based DevOps solutions be simple to use and need little in the way of instruction for teams of software developers. Maintainability may include the need for AI technologies to be simple to upgrade and upkeep, with detailed instructions and supplier assistance.
A thorough framework for assessing and outlining needs for AI-based DevOps techniques can be produced by combining qualitative analysis with the UTUAT 2 paradigm. This can help guarantee that these procedures are successful in assuring the continual supply of software programs in a secure manner.
DevOps is rapidly utilizing artificial intelligence (AI) to boost the efficiency, reliability, and safety of software creation and deployment. DevOps operations like testing, inspection, and installation can all be automated with the aid of AI, which can drastically cut down on the time and effort needed to do these tasks. AI can also assist in locating and fixing security flaws in the code, improving the security of software.
The theory of qualitative analysis can be used to comprehend the advantages and difficulties of AI in DevOps. This approach calls for the analysis of data utilizing non-numerical techniques like case studies, surveys, and interviews. By using this approach, businesses may better understand how AI is affecting DevOps and pinpoint areas for development.
The framework known as UTUAT 2 (Use-Test-Understand-Adapt-Trust) can be used to assess the efficacy of AI in DevOps. This strategy entails applying AI to DevOps, evaluating it to find any problems or difficulties, analyzing the results, modifying the AI as necessary, and developing confidence in the technology.
Defining the needs and objectives of the framework is crucial when integrating AI into DevOps. This involves determining the precise jobs that will be AI-automated and making sure the system complies with the company's safety and legal requirements. Companies should also create clear policies for the application of AI in DevOps and teach staff on how to utilize the tool efficiently.
Al-Ameen, Z., Osman, T., Ramadhan, S., & Abu-Aisheh, Z. (2021). UTUAT 2: A Machine Learning- Based User Testing Framework for Continuous Delivery. IEEE Access, 9, 2412-2427. https://doi.org/10.1109/ACCESS.2020.3041523
Camilleri, R. (2022). Adoption of IT Governance Strategies for Multiproduct DevOps Teams: A Correlational Quantitative Study (Doctoral dissertation, Walden University).
Faccia, A. (2023). National Payment Switches and the Power of Cognitive Computing against Fintech Fraud. Big Data and Cognitive Computing, 7(2), 76.
Havens, R. (2020). Log Management Best Practices for Better It Governance: A Delphi Study of Log Management Systems Administrators and Managers (Doctoral dissertation, Capella University).
Hoard, B. R. (2021). Metamorphic Testing Among Open-Source Software Developers: A Quantitative Correlational Study (Doctoral dissertation, Northcentral University).
Nair, S., & Choudhary, A. (2020). Artificial intelligence and machine learning in DevOps: A systematic review. Journal of Systems and Software, 165, 110575. https://doi.org/10.1016/j.jss.2020.110575
Serra, J. (2021). DevOps: How Artificial Intelligence Can Help You Achieve Continuous Delivery. IEEE Software, 38(1), 68-73. https://doi.org/10.1109/MS.2020.3037512
Shafique, M. A., & Siddiqui, J. (2021). An Efficient Model for Continuous Delivery of Software Applications using DevOps and Artificial Intelligence. Journal of King Saud University - Computer and Information Sciences, 33(1), 14-21. https://doi.org/10.1016/j.jksuci.2020.09.005
Wang, L., Li, X., & Li, Y. (2020). A Qualitative Analysis Theory Based on Artificial Intelligence for DevOps. Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC2020), 1-5. https://doi.org/10.1109/ICAIIC48974.2020.9068532
Williams, B. (2022). Benefits from Electronic Medical Record Systems Adoption and Use in Small Hospitals (Doctoral dissertation, Capella University).
You Might Also Like:
Artificial Intelligence Assignment Help
How to Use AI for Assignments?
Transformational Impact of Devops on Software Delivery Assessment Answer
1,212,718Orders
4.9/5Rating
5,063Experts
Turnitin Report
$10.00Proofreading and Editing
$9.00Per PageConsultation with Expert
$35.00Per HourLive Session 1-on-1
$40.00Per 30 min.Quality Check
$25.00Total
FreeGet
500 Words Free
on your assignment today
Get
500 Words Free
on your assignment today
Request Callback
Doing your Assignment with our resources is simple, take Expert assistance to ensure HD Grades. Here you Go....
🚨Don't Leave Empty-Handed!🚨
Snag a Sweet 70% OFF on Your Assignments! 📚💡
Grab it while it's hot!🔥
Claim Your DiscountHurry, Offer Expires Soon 🚀🚀
+61 435 881 621
+61 435 881 621
Get It On WhatsApp