Journal: ACM Transactions on Software Engineering and Methodology (TOSEM)
Manuscript ID: TOSEM-2026-0481
Status: Requires reformatting and resubmission
Type: Single-blind (authors identified, not anonymized)
- All authors listed on first page
- Full names (not anonymized)
- Author affiliations
- Corresponding author contact info
- ORCID numbers (optional but recommended)
- Title page with author info
- Abstract (150-250 words)
- Introduction
- Related Work
- Methodology
- Results & Analysis
- Discussion
- Conclusions
- Acknowledgments
- References
- Appendices (if needed)
- ACM template (Word or LaTeX)
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Title: Large-Scale Analysis of AI Context Extraction Across 240 Open-Source Repositories
Authors:
- Manoj Mallick, [Affiliation]
Email: [email]
ORCID: [if available]
Corresponding Author:
- Manoj Mallick
Email: [email]
Phone: [phone]
Acknowledgments:
[List collaborators, funding sources, etc.]
Focus:
- Problem: Token limits in LLM context management
- Solution: SigMap - systematic evaluation of context extraction
- Dataset: 405 open-source repositories, 30+ languages
- Key findings: 96.2% token reduction, language patterns matter
- Contribution: Empirical foundation for context extraction research
- Data availability: CC-BY-4.0 licensed, publicly available
Keywords: Context extraction, LLM optimization, Code compression,
Empirical software engineering, AI-assisted development
Sections:
1.1 The Problem
- LLM token limits
- Context management challenge
- Industry relevance
1.2 Motivation
- Why context extraction matters
- Gap in empirical knowledge
- Opportunity for systematic study
1.3 Our Approach
- Large-scale evaluation
- 405 diverse repositories
- Reproducible methodology
1.4 Key Contributions
- Largest empirical study of context extraction
- Language-specific patterns identified
- Monorepo optimization insights
- Complete reproducibility package
1.5 Paper Organization
Sections:
2.1 Context Management in LLMs
- Token limits and their impact
- Existing approaches
- Limitations of current methods
2.2 Code Compression & Summarization
- Prior work on code summarization
- Token reduction techniques
- Language-specific approaches
2.3 Empirical Software Engineering
- Large-scale code analysis studies
- Multi-language evaluation
- Reproducibility in SE research
2.4 AI-Assisted Software Engineering
- LLM integration in development
- Context optimization
- Tool effectiveness
2.5 Gap Analysis
- What's missing from prior work
- Why systematic study needed
- Our contribution
Sections:
3.1 Research Questions
- RQ1: How effective is context extraction across languages?
- RQ2: What factors affect compression rates?
- RQ3: Can findings generalize?
- RQ4: What's the practical impact?
3.2 Dataset & Repository Selection
- Stratified sampling approach
- 405 repositories selected
- Language distribution
- Size distribution
- Diversity metrics
3.3 Execution Environment
- Hardware specs (c2-standard-8)
- Software stack
- SigMap tool version
- Network conditions
3.4 Benchmark Modes
- Health check
- Baseline measurement
- Standard analysis
- Comprehensive reporting
- Deep analysis
- Mode parameters
3.5 Metrics & Measurement
- Token reduction percentage
- Compression ratio
- Execution time
- Success rate
- Data completeness
3.6 Quality Assurance
- Validation procedures
- Error handling
- Data integrity checks
- Reproducibility verification
Sections:
4.1 Overall Statistics
- 405 repositories analyzed
- 2,025+ operations executed
- 96.2% average token reduction
- Success rate: 99.6%
- Data completeness: 100%
4.2 Language-Specific Analysis
- Python results (96.2% ± 1.8%)
- JavaScript results (92.1% ± 4.2%)
- Go, Rust, Java results
- Comparative analysis
- Statistical significance
4.3 Repository Size Analysis
- Token reduction by size
- Size doesn't predict compression
- Other factors more important
4.4 Monorepo Analysis
- 45 monorepos identified
- Specialized handling impact
- 2-3% improvement potential
- Module count analysis
4.5 Framework Impact
- Framework-specific patterns
- React vs Vue (JavaScript)
- Django vs FastAPI (Python)
- Design pattern effects
4.6 Methodology Validation
- Published (240) vs Extended (405) comparison
- Identical 96.2% average
- Findings generalize
- Methodology robustness
Sections:
5.1 Interpretation of Findings
- Language organization matters
- Code idioms primary factor
- Framework diversity effects
- Size is not determinant
5.2 Implications for Researchers
- Baseline for future work
- Language-specific optimization
- Research directions
5.3 Implications for Tool Builders
- Realistic token reduction targets
- Monorepo optimization opportunity
- Language-specific strategies
5.4 Limitations
- Shallow clones (depth=1)
- Single execution run
- Tool-specific results
- Language detection limitations
- 300-second timeout
5.5 Threats to Validity
- Selection: diverse sampling
- Measurement: documented metrics
- Construct: multiple benchmark modes
- Internal: controlled environment
- External: 405 repos, 30+ languages
5.6 Generalizability
- Findings hold across sample sizes
- Multiple languages represented
- Open-source + production repos
- Different domains covered
- Summary of contributions
- Key findings recap
- Implications
- Future work opportunities
- Impact on field
Funding sources
Collaborators
Tool authors (SigMap team)
Infrastructure providers
Community contributors
Format: ACM style
Key papers to cite:
- LLM context management
- Code summarization
- Software engineering empirical studies
- Reproducible research
- AI-assisted development
- Complete dataset (CSV, JSON, JSONL, SQL)
- Reproducibility scripts
- Hardware configuration details
- Language statistics
- Benchmark output samples
- Extended tables/charts
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First Author Name, Affiliation
Second Author Name, Affiliation
Corresponding Author: Name, Email, Phone
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ACM TOSEM:
- Journal: https://tosem.acm.org/
- Author Guidelines: https://tosem.acm.org/authors.cfm
- Template Download: https://www.acm.org/publications/submission-templates
ACM Style:
- Reference style: https://www.acm.org/publications/authors/reference-formatting
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Supplementary:
- Data deposit options: Zenodo, Figshare, GitHub
- Open science best practices: www.cos.io
- Reproducibility guidance: https://www.acm.org/publications/policies/artifact-review-and-badging-current
Dear Editor-in-Chief,
We are submitting a revised manuscript titled "Large-Scale Analysis of AI
Context Extraction Across 240 Open-Source Repositories" for publication in
ACM Transactions on Software Engineering and Methodology.
[Brief description of contribution]
This work:
- Provides empirical foundation for context extraction research
- Includes complete reproducible methodology
- Releases public dataset (CC-BY-4.0)
- Identifies language-specific optimization opportunities
- Validates findings across 240→405 repositories
All required materials are included:
✓ Reformatted manuscript (ACM template)
✓ Author information (non-anonymized)
✓ Complete datasets (CSV, JSON, JSONL, SQL)
✓ Reproducibility scripts
✓ Research papers (primary + extended)
We have no conflicts of interest to declare.
The authors are: [List all authors with affiliations]
Corresponding author: [Name, email, phone]
Thank you for considering this submission.
Sincerely,
Manoj Mallick
[Affiliation]
- Used ACM template (not your own format)
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- 10pt+ font, readable
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- Abstract: 150-250 words
- Introduction: ~1000-1200 words
- Related work: ~800-1000 words
- Methodology: ~1200-1500 words
- Results: ~1500-2000 words
- Discussion: ~800-1200 words
- Conclusions: ~300-500 words
- References: 30-50 citations
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