Framework for Integrating Requirements Engineering and DevOps Practices in Robotic Process Automation with a Focus on Optimizing Human–Computer Interaction
Abstract
:1. Introduction
2. Background
- Challenges in Requirements Engineering: The complexity of defining requirements for RPA processes can be a significant impediment, particularly as these requirements may evolve throughout the project’s lifecycle. Ambiguities or a lack of detail in requirement specifications can adversely affect RPA implementation. Additionally, the dynamic nature of requirements necessitates a flexible and adaptive approach to Requirements Engineering [23].
- Challenges of DevOps in RPA Projects: Adapting traditional software development methodologies to accommodate the specific demands of RPA presents further challenges. The automation of testing and monitoring, as well as the continuous deployment of RPA bots, necessitates specialized tools and methodologies, as well as the capability to manage and oversee bot updates in real time. Continuous collaboration between development and operations teams is essential in order to address the complexity and frequency of updates [23].
- Challenges in human–computer interaction: The interaction between humans and bots may be hindered if RPA systems are not optimally designed, leading to issues such as poor usability, lack of transparency, and diminished trust in automated bots. Addressing these challenges requires meticulous interface design and the implementation of HCI best practices to ensure an optimal user experience, thereby fostering user acceptance and the effective utilization of RPA bots [23].
3. Methodology
3.1. Method
- Articles that directly discuss the implementation or evaluation of RPA and RE and DevOps practices in process automation, focusing on optimizing human–computer interaction.
- Studies conducted between 2010 and 2025, ensuring the relevance and timeliness of the practices and technologies discussed.
- Articles published in peer-reviewed scientific journals and available in full text.
- Articles that do not directly address the implementation of RPA, RE, and DevOps or human–computer interaction, or that focus on areas unrelated to process automation.
- Works that do not significantly discuss aspects of optimizing human–computer interaction in RPA systems or that do not address integration with RE and/or DevOps practices.
- Publications that are inaccessible in full text or with restricted access.
- Studies that are not based on empirical methodologies or that do not present clear and consistent data on the impacts of integrating RPA, RE, DevOps, and HCI on the development and operation of automated systems.
- Restriction to peer-reviewed articles, reducing the number to 410 articles.
- The limitation of the publication period, considering only articles published between 2010 and 2025, which results in 349 articles.
- Selection by language, keeping only articles in English, which reduces the total to 318 articles.
- Restriction to articles with full text available, leaving 284 articles for analysis.
3.2. Articles Synthesis and Analysis
3.3. Discussion of Literature Review Results
- Human–computer interaction (HCI): featured in 27% of the article, this theme is primarily explored in the context of improving user experience and optimizing human interaction with automated systems.
- Robotic process automation (RPA): this most prevalent theme, appearing in 47% of the articles.
- DevOps: discussed in 33% of the articles, this theme focuses on the integration of automation into the software development lifecycle.
- Requirements Engineering (RE): identified in 27% of the articles, this theme explores the need for detailed Requirements Engineering in the development of automated systems.
- Synergies between the themes: The intersection of RPA with HCI and DevOps stands out as a key area of focus. RPA systems benefit significantly from HCI principles, which help in designing more user-friendly interfaces, thereby improving system adoption and efficiency. Furthermore, the integration of DevOps practices in RPA development enables continuous integration and deployment, ensuring that automation solutions are scalable and adaptable.
- Challenges in RE: Requirements Engineering (RE) is frequently discussed in relation to the early stages of system development, especially in RPA projects.
- Lack of unified frameworks: While the individual themes are well explored, there is a lack of a comprehensive approach that integrates HCI, RPA, DevOps, and RE in a cohesive manner. The development of such frameworks could provide more practical guidance for the implementation of automation systems.
- Industry-specific applications: many of the studies focus on general applications of RPA, with limited exploration of sector-specific adaptations.
4. The Proposed Method
4.1. Proposal for a Framework (FRIDA)
- Analysis: At this stage, the focus is on understanding the process that will be automated. The RPA team analyzes the current workflow, identifies repetitive, manual, and time-consuming tasks, and assesses the feasibility of automation. It is important to determine whether the process is stable, well documented, and suitable for RPA.
- ○
- Objective: identify processes that are candidates for automation and ensure that automation will bring value to the business.
- ○
- Results: list of priority processes for automation and a clear vision of the expected benefits.
- Requirement gathering: Here, the team details the requirements of the process that will be automated. This includes understanding the business rules, the systems involved, the exceptions that can occur, and the expected results. Collaboration with end-users and stakeholders is crucial to ensure all aspects of the process are considered.
- ○
- Objective: clearly define what the robot should do and how it should behave in different scenarios.
- ○
- Results: document functional and non-functional requirements, which will serve as a basis for development.
- Design–project development: In this phase, the RPA team creates the automation design, including the robot workflow, decision logic, and integration with existing systems. RPA tools are used to develop the robot, following best programming and automation practices.
- ○
- Objective: build a functional robot that meets the defined requirements.
- ○
- Results: a prototype or initial version of the robot, ready for testing.
- Testing phase: The developed robot is tested in a controlled environment to ensure that it works as expected. This includes unit tests (for each part of the robot), integration tests (to verify interaction with other systems), and acceptance tests (to validate with end-users).
- ○
- Objective: identify and correct errors before deployment into production.
- ○
- Results: robot validated and approved for implementation.
- Deployment and hypercare: After testing, the robot is deployed in the production environment. The hypercare phase is a period of intensive monitoring right after deployment, where the RPA team keeps an eye out for any issues or necessary adjustments.
- ○
- Objective: ensure that the robot functions correctly in production and quickly resolve any problems that arise.
- ○
- Results: robot in stable operation and continuous support during the hypercare period.
- Go-live and Support: At this stage, the robot is fully operational and begins performing its tasks in the production environment. Ongoing support is provided to deal with any issues, updates or process changes. Additionally, the robot’s performance is monitored to ensure it continues to deliver the expected results.
- ○
- Objective: keep automation running efficiently and aligned with business needs.
- ○
- Results: fully functioning automation, with ongoing support and continuous improvement if needed.
- 2.
- 3.
- 4.
- 5.
- 6.
4.2. Characteristics and Benefits of the Framework (FRIDA)
- Holistic lifecycle approach: FRIDA covers the entire RPA lifecycle, from initial analysis to ongoing support. By embedding RE and DevOps practices in every phase, it ensures that automation projects are planned, implemented, and maintained comprehensively.
- Structured yet flexible design: The framework provides a well-defined structure, while allowing flexibility to adapt to varying project requirements and organizational needs. This balance ensures relevance across industries and project scales.
- Integration of key practices:
- Requirements engineering: focuses on capturing detailed, business-aligned requirements to guide automation projects effectively.
- DevOps methodologies: ensure continuous integration, testing, and delivery while fostering collaboration between development and operational teams.
- Emphasis on human–computer interaction (HCI): FRIDA prioritizes creating intuitive and user-friendly systems, optimizing interactions between users and automated processes.
- Modularity and scalability: The framework advocates for modular design principles, ensuring that automation solutions are scalable and maintainable. This approach supports future process enhancements and adaptability to evolving business needs.
- Performance monitoring and continuous feedback: real-time monitoring tools and continuous feedback loops are embedded in the framework to ensure optimal performance and alignment with business objectives.
- Sustainability and evolution: FRIDA is designed to evolve with organizational changes, making automation solutions sustainable over time. This characteristic supports long-term value creation and adaptability.
- Increased operational efficiency: By streamlining the automation lifecycle, FRIDA reduces development time and operational overhead, enabling organizations to achieve faster time-to-value.
- Improved collaboration: The integration of RE and DevOps fosters enhanced collaboration between stakeholders, including developers, operations teams, and business units. This alignment reduces silos and improves project outcomes.
- Enhanced user experience: the focus on HCI ensures that automation solutions are intuitive and user-friendly, reducing resistance to adoption and improving overall satisfaction.
- Minimized risks and errors: the structured approach of FRIDA, combined with rigorous testing and monitoring practices, mitigates risks associated with automation deployment and maintenance.
- Scalability and futureproofing: the modular design of FRIDA enables organizations to scale automation solutions seamlessly, supporting business growth and future technological advancements.
- Sustainable automation: by promoting adaptability and continuous improvement, the framework ensures that automation solutions remain relevant and valuable, even as organizational needs evolve.
- Data-driven decision-making: through performance monitoring and real-time analytics, FRIDA empowers organizations to make informed decisions about optimizing and expanding their automation initiatives.
- Alignment with business goals: By embedding RE principles, FRIDA ensures that automation projects are closely aligned with strategic objectives, delivering solutions that drive measurable business impact.
5. Case Study
5.1. Case Study Presentation
- Inefficiencies in processing time:
- Each application takes an average of 2 h to process fully, leading to delays in meeting customer expectations.
- Bottlenecks often occur during peak periods, such as the end of fiscal quarters.
- High error rates:
- Manual data entry errors lead to incorrect assessments or rejections of valid applications.
- Approximately 8% of applications require rework due to inconsistencies in documentation validation.
- Strained employee workloads:
- Employees report high levels of stress due to repetitive tasks and the pressure to meet tight deadlines.
- Turnover rates have increased, with 3 employees leaving in the past year.
- Lack of scalability:
- As the company expands, the current processes struggle to keep up with the growing volume of applications.
- Management has expressed concerns about the long-term sustainability of the current approach.
- Automate repetitive tasks such as data entry, document validation, and compliance checks.
- Provide employees with intuitive dashboards to monitor and intervene in processes as needed.
- Reduce processing times while maintaining high accuracy levels.
- Enhance employee satisfaction by allowing them to focus on higher-value tasks.
- Analyze the inefficiencies and challenges in the current manual processes.
- Evaluate the impact of implementing FRIDA on the development and deployment of an RPA system.
- Compare the performance metrics before and after the framework’s implementation, focusing on the following aspects:
- Processing time per application.
- Error rates and rework percentages.
- Scalability and adaptability of the automation solution.
5.2. Application of the Framework to the Case Study
- Phase 1: Analysis
- Phase 2: Requirement Gathering
- Phase 3: Design–Project Development
- Phase 4: Testing Phase
- Phase 5: Implementation and Hypercare
- Phase 6: Go-live and Support
6. Analysis of Results and Discussion
6.1. Analysis of Results
- Processing time per application (time spent processing each credit application);
- Error rate (percentage of errors in the application processing);
- Manual vs. automated steps (comparison of tasks performed manually versus automated);
- Compliance rate (the rate at which processed applications met regulatory requirements);
- System scalability (ability to handle increased volumes of applications).
- Processing time per application: Data were collected from the company’s internal CRM system and process logs before and after automation. Time was measured from the receipt of the application to final approval or rejection.
- Error rate: The error rate was tracked through manual records in the credit application system, identifying inconsistencies in data entry and validation before automation. After implementation, the error rate was monitored through the automated system’s logs and feedback from the compliance team.
- Manual vs. automated steps: The analysis of manual and automated processes was conducted based on detailed documentation of work processes and logs generated by RPA (Robotic Process Automation) software. To identify which steps were automated and which remained manual, we used specific tools such as UiPath for automation. This tool was chosen due to its wide adoption in the market and its ability for integration with existing systems, such as the CRM used by the organization. In the case of this study, the CRM in question was Salesforce, a widely used platform for customer relationship management. Salesforce was chosen for its flexibility and customizability, although its native integration with RPA required adaptations due to the lack of specific APIs for automating financial processes, such as loan application processing. To overcome these limitations, DevOps practices were incorporated into the automation workflow, aiming for more efficient integration between RPA and CRM. Tools like Jenkins were used to orchestrate continuous integration (CI/CD) pipelines, enabling test automation and the deployment of RPA scripts in an agile and reliable manner. Additionally, Git was used to establish versions of automation scripts, ensuring traceability and collaboration between development and operations teams. This approach allowed for the creation of an environment where updates to Salesforce or RPA processes could be implemented quickly, with a reduced risk of errors and greater consistency. The integration between RPA and DevOps was not limited to the automation of repetitive tasks, but also included the application of principles such as continuous monitoring and feedback loops. For example, logs generated by UiPath have been consolidated into dashboards in Jenkins, enabling the proactive identification of failures and continuous process optimization. This synergy between RPA and DevOps has not only accelerated loan application processing but also created a foundation for future evolution, where automation can be expanded to other workflows, such as credit analysis and financial reporting. This approach allowed for a clear analysis of the gaps between manual and automated processes, highlighting future opportunities for greater integration between systems and the adoption of more advanced automation practices. The incorporation of DevOps in its strictest sense, with the use of tools such as Jenkins and Git, demonstrates how the organization can evolve towards a more integrated software development model, where the automation of business processes and the continuous delivery of technological solutions go hand in hand.
- Compliance rate: Compliance data were obtained from the financial compliance verification system, which tracks the regulatory requirements for each application. The rate of compliance was calculated based on successful completions of the automated and manual processes.
- System scalability: This was measured based on the volume of applications processed per day. Data were obtained from both manual and automated systems, showing the system’s capacity to handle increased loads after automation.
6.2. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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P | (Population) Group of interest in the study. |
I | (Intervention) Exposure or factor analyzed. |
C | (Comparison) Comparison with another intervention or control group. |
O | (Outcome) Expected or measured result of the study. |
Group 1 | Group 2 | Group 3 | Group 4 |
---|---|---|---|
“RPA” OR “Robotic Process Automation” OR “Intelligent Process Automation” OR “Digital Process Automation” OR “Business Workflow Automation” | “Requirements Engineering” OR “Requirement Analysis” OR “System Requirements” OR “Requirement Specification” OR “Requirement Validation” OR “Requirement Management” OR “Functional Requirements” OR “RE” | “DevOps Practices” OR “Continuous Integration” OR “Continuous Deployment” OR “Infrastructure as Code” OR “Agile Development” OR “DevOps Automation” OR “CI/CD Pipeline” OR “DevOps” | “Human-Computer Interaction” OR “User Experience” OR “Interface Design” OR “Usability Testing” OR “Interaction Design” OR “User-Centered Design” OR “Cognitive Load” OR “HCI” |
Set 1 | Set 2 | Set 3 | |
---|---|---|---|
Initial result: | 806 | 18 | 51 |
1—Restrict to peer-reviewed | 378 | 18 | 14 |
2—From 2010 to 2025 | 318 | 18 | 13 |
3—Language: English | 299 | 13 | 6 |
4—Restrict to full text | 274 | 7 | 3 |
Themes of the Articles | HCI | RPA | DevOps | RE | |
---|---|---|---|---|---|
Articles | |||||
[29] | X | X | |||
[30] | X | ||||
[31] | X | ||||
[32] | X | ||||
[33] | X | ||||
[34] | X | X | |||
[35] | X | ||||
[36] | X | X | |||
[37] | X | ||||
[38] | X | ||||
[39] | X | ||||
[40] | X | ||||
[41] | X | X | |||
[42] | X | ||||
[43] | X | X | |||
% Themes p/articles | 27% | 47% | 33% | 27% |
RE Criteria: Analysis |
---|
The identification of the processes to be automated and their boundaries. |
The assessment of process complexity and its business impact. |
The evaluation of the technical and economic feasibility of automation. |
The identification of integrations with existing systems. |
The establishment of ROI indicators to justify the project. |
DevOps Criteria: Analysis |
---|
Collaboration between developers and operations, ensuring clear communication from project inception. |
Capacity planning to evaluate the computational resources required for automation. |
Continuous optimization, identifying areas for improvement from the outset to enhance the efficiency of automated processes. |
RE Criteria: Requirement Gathering |
---|
Ensuring the process is structured, repetitive, and governed by clear rules. |
Detailing interactions with systems, interfaces, and files. |
The specification of performance, security, and compliance requirements. |
Alignment with stakeholders to collect specific project requirements. |
DevOps Criteria: Requirement Gathering |
---|
The versioning of scripts and assets to manage changes effectively. |
Controlled change management processes for evolving requirements. |
Early planning of unit and integration tests. |
RE Criteria: Design–Project Development |
---|
Ensuring resilience to errors or system failures. |
Planning solutions for exceptions and legacy system integration issues. |
Considering compatibility and scalability requirements for future processes. |
DevOps Criteria: Design–Project Development |
---|
Modular architecture to facilitate maintenance and scalability. |
Implementation of CI/CD pipelines for automation of building, testing, and deploying bots. |
Comprehensive documentation and continuous feedback during development. |
RE Criteria: Testing Phase |
---|
Planning test scenarios to validate automations. |
Defining performance metrics and monitoring methods. |
DevOps Criteria: Testing Phase |
---|
Implementation of automated testing and production environment simulations. |
Ensuring test coverage for use cases, exceptions, and edge conditions. |
Planning redundancy solutions and load balancing mechanisms. |
RE Criteria: Deployment and Hypercare |
---|
Defining strategies for the robot’s continuous maintenance and updates. |
Specifying performance metrics and error reduction strategies. |
DevOps Criteria: Deployment and Hypercare |
---|
Implementing real-time monitoring and proactive alerts. |
Measuring KPIs such as success rates and average execution times. |
Securely managing credentials and auditing bot actions. |
RE Criteria: Go-Live and Support |
---|
Addressing limitations of available RPA tools. |
Defining scalability criteria for replicable processes. |
DevOps Criteria: Go-Live and Support |
---|
Ensuring consistency across production, development, and testing environments. |
Implementing redundancy to maintain continuity during failures. |
Monitoring and optimizing bot performance based on established KPIs and metrics. |
Before Implementation (Manual Process) | After Implementation (Automated Process) | Improvement (%) | |
---|---|---|---|
Processing time per application | 120 min | 20 min | 83% |
Error rate | 8% | 1.5% | 81.25% |
Manual steps | 100% of tasks manually performed | 20% of tasks automated | 80% reduction |
Compliance rate | 92% | 98% | 6.5% improvement |
System scalability | Limited scalability (manual capacity) | Highly scalable, handling higher volumes | Significant increase |
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Share and Cite
Patrício, L.; Varela, L.; Silveira, Z. Framework for Integrating Requirements Engineering and DevOps Practices in Robotic Process Automation with a Focus on Optimizing Human–Computer Interaction. Appl. Sci. 2025, 15, 3485. https://doi.org/10.3390/app15073485
Patrício L, Varela L, Silveira Z. Framework for Integrating Requirements Engineering and DevOps Practices in Robotic Process Automation with a Focus on Optimizing Human–Computer Interaction. Applied Sciences. 2025; 15(7):3485. https://doi.org/10.3390/app15073485
Chicago/Turabian StylePatrício, Leonel, Leonilde Varela, and Zilda Silveira. 2025. "Framework for Integrating Requirements Engineering and DevOps Practices in Robotic Process Automation with a Focus on Optimizing Human–Computer Interaction" Applied Sciences 15, no. 7: 3485. https://doi.org/10.3390/app15073485
APA StylePatrício, L., Varela, L., & Silveira, Z. (2025). Framework for Integrating Requirements Engineering and DevOps Practices in Robotic Process Automation with a Focus on Optimizing Human–Computer Interaction. Applied Sciences, 15(7), 3485. https://doi.org/10.3390/app15073485