name: parsifal-slr-guide description: "Plan and manage systematic literature reviews with Parsifal platform" metadata: openclaw: emoji: "📋" category: "research" subcategory: "methodology" keywords: ["Parsifal", "systematic review", "SLR", "review protocol", "PICO", "research methodology"] source: "https://github.com/vitorfs/parsifal"

Parsifal Systematic Literature Review Guide

Overview

Parsifal is a web-based tool for planning and managing systematic literature reviews (SLRs) following established protocols (Kitchenham, PRISMA). It guides researchers through the complete SLR process: defining research questions, setting inclusion/exclusion criteria, planning search strings, and tracking the screening process. Open-source and self-hostable.

SLR Process with Parsifal

Phase 1: Planning

Define Research Questions

Structure questions using PICO framework:

• Population: What group/domain?
• Intervention: What technique/method?
• Comparison: Compared to what?
• Outcome: What results measured?

Example:
P: Software development teams
I: AI-assisted code review
C: Manual code review
O: Defect detection rate, review time

Research Questions:
RQ1: Does AI-assisted code review improve defect detection?
RQ2: What is the time savings compared to manual review?
RQ3: What types of defects are best detected by AI tools?

Set Criteria

Inclusion Criteria:
IC1: Studies comparing AI vs manual code review
IC2: Published in peer-reviewed venues (2020-2026)
IC3: Reports quantitative metrics

Exclusion Criteria:
EC1: Grey literature / blog posts
EC2: Studies with fewer than 10 participants
EC3: Non-English publications

Phase 2: Search Strategy

Build Search String

("artificial intelligence" OR "machine learning" OR "deep learning")
AND
("code review" OR "code inspection" OR "static analysis")
AND
("defect detection" OR "bug finding" OR "software quality")

Database Mapping

Phase 3: Selection

Screening Steps

1. Remove duplicates — Match by DOI, title similarity
2. Title screening — Quick relevance assessment
3. Abstract screening — Apply inclusion/exclusion criteria
4. Full-text review — Detailed evaluation

Quality Assessment

Define quality criteria and scoring:

Phase 4: Extraction

Data Extraction Form

For each included paper, extract:
- Study ID
- Authors, Year, Venue
- Study type (experiment/case study/survey)
- Population size
- AI technique used
- Metrics reported (precision, recall, F1, time)
- Key findings
- Limitations noted

Phase 5: Synthesis

Report with PRISMA

Identification: 1,400 records
  ↓ Remove duplicates: -350
Screening: 1,050 titles/abstracts
  ↓ Exclude irrelevant: -900
Eligibility: 150 full-text assessed
  ↓ Exclude by criteria: -108
Included: 42 studies in final review

Self-Hosting Parsifal

git clone https://github.com/vitorfs/parsifal.git
cd parsifal
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver
# Access at http://localhost:8000

SLR Best Practices

1. Register protocol before starting (PROSPERO for health, OSF for others)
2. Two independent reviewers for screening to reduce bias
3. Track inter-rater agreement (Cohen's kappa > 0.8)
4. Document deviations from the original protocol
5. Use PRISMA checklist for reporting completeness

References

• Parsifal
• Kitchenham, B. & Charters, S. (2007). "Guidelines for performing Systematic Literature Reviews in Software Engineering."
• PRISMA Statement
• PROSPERO Registry