Developing a framework to prioritize software requirements using AI-based techniques is an innovative and practical research area in software engineering.

1. Problem Identification

Research Problem:

• Software requirements prioritization is often subjective, time-consuming, and prone to inconsistencies, especially in large-scale projects.
• Traditional prioritization techniques (e.g., MoSCoW, Kano, and pairwise comparisons) may not scale well with complex datasets or accommodate dynamic changes in stakeholder preferences.

Research Question:

• How can an AI-based framework improve the efficiency, consistency, and adaptability of software requirements prioritization?

2. Objectives of the Framework

• Develop a framework that utilizes AI techniques (e.g., machine learning, natural language processing, or multi-criteria decision-making models) to prioritize requirements.
• Incorporate stakeholder preferences, dependencies, and constraints into the prioritization process.
• Provide an adaptive and scalable solution for evolving requirements in agile and traditional software development environments.

3. Artifact Design

Key Components of the Framework:

1. Requirement Data Collection Module:
   • Input: Collect requirements, stakeholder preferences, and project constraints.
   • Use NLP to analyze and preprocess textual requirements.
2. Feature Extraction Module:
   • Extract relevant attributes (e.g., cost, risk, stakeholder value, urgency, feasibility).
   • Use dependency mapping to identify interrelations between requirements.
3. AI Prioritization Engine:
   • Techniques:
     • Supervised Learning: Train models on historical prioritization decisions.
     • Reinforcement Learning: Optimize decisions based on iterative feedback.
     • Multi-Criteria Decision Analysis (MCDA): Use AI to balance multiple criteria.
   • Outputs: Assign priority scores to each requirement.
4. Visualization and Feedback Module:
   • Generate visual reports (e.g., priority heatmaps or dependency graphs).
   • Collect stakeholder feedback for iterative improvements.

4. Evaluation Plan

Evaluation Metrics:

• Accuracy: Compare AI-based prioritization results with expert decisions.
• Efficiency: Measure time saved compared to traditional techniques.
• Stakeholder Satisfaction: Use surveys to evaluate stakeholder trust in AI recommendations.
• Scalability: Test the framework with varying numbers of requirements.

Evaluation Methods:

• Case Studies: Apply the framework to real-world projects.
• Simulations: Use synthetic datasets to test performance under controlled conditions.
• Expert Validation: Collaborate with software project managers and developers to assess the results.

5. Iterative Refinement

• Gather feedback from evaluation results to refine the framework.
• Address any limitations, such as inaccurate predictions or usability issues.

6. Research Contribution

Expected Contributions:

• Practical Contribution: A scalable, AI-driven tool that simplifies the prioritization process for software teams.
• Theoretical Contribution: New insights into integrating AI with software requirements engineering.
• Methodological Contribution: A reproducible framework and methodology for prioritizing requirements using AI.

7. Implementation Plan

Tools and Technologies:

• NLP: Python libraries like spaCy or NLTK for text preprocessing.
• Machine Learning: Scikit-learn, TensorFlow, or PyTorch for model development.
• Visualization: Tableau or Matplotlib for priority reports.
• Data Storage: Use databases like PostgreSQL to manage requirement data.

Workflow:

1. Collect requirements data from past or ongoing software projects.
2. Develop the AI engine to analyze and prioritize requirements.
3. Integrate the prioritization engine with project management tools (e.g., JIRA, Trello).

8. Potential Challenges

• Data Availability: Limited access to historical prioritization data.
• Resistance to Change: Stakeholders may mistrust AI-based decisions.
• Complexity: Balancing multiple criteria while maintaining transparency.

9. Example Use Case

Scenario:

A software development team is working on an e-commerce application. They need to prioritize 100+ requirements based on factors like user impact, implementation cost, and urgency.

Framework Application:

1. Collect and preprocess requirements data.
2. Use the AI engine to assign priority scores.
3. Generate a visual report ranking the requirements.
4. Allow stakeholders to review and adjust scores if needed.