Commit Message Analysis: Recommending Refactorings via Developer Intent Understanding

Published in: Information and Software Technology (IST) Journal, 2020

The Context Problem in Refactoring

Traditional refactoring recommendation approaches have a fundamental limitation: they ignore developer context and intention. While detecting code smells is valuable, many identified opportunities may be irrelevant to what developers are actually trying to achieve.

The Traditional Approach vs. Reality

❌ Limitations of Current Methods

Static & Dynamic Analysis Approaches:

Focus solely on code structure and metrics
Identify refactoring opportunities in isolation
Miss developer context and intentions
Generate recommendations that may not align with current goals

The Gap: Recent studies reveal that while developers document their refactoring intentions in commit messages, they often miss relevant refactorings aligned with their stated rationale.

✅ Our Innovation: Context-Driven Refactoring

We bridge this gap by analyzing developer commit messages to understand intent and provide context-aware refactoring recommendations.

Approach Overview

Our approach: From commit message analysis to context-driven refactoring recommendations

Our Methodology: Mining Developer Intent

🔍 Step 1: Commit Message Analysis

Natural Language Processing of developer commit messages
Intent extraction from documented refactoring activities
Pattern recognition in developer communication

📊 Step 2: Quality Impact Assessment

Analyze quality improvements in changed files
Correlate intent with actual quality outcomes
Validate effectiveness of developer-stated goals

🧠 Step 3: Knowledge Distillation

Transform insights into actionable recommendations
Context-driven suggestions aligned with developer goals
Complement existing static and dynamic analysis tools

⚡ Step 4: Intelligent Recommendation

Real-time analysis of current commit messages
Contextual suggestions based on stated intentions
Quality-validated recommendations with proven impact

Key Innovation: Developer Intent as Context

💡 The Insight

Developers often state their refactoring intentions but miss opportunities to fully realize their goals. Our approach:

Learns from past behavior - Analyzes historical commit messages
Understands current intent - Processes current commit descriptions
Suggests aligned refactorings - Recommends actions that match stated goals
Validates quality impact - Ensures suggestions improve code quality

🎯 Context-Aware Recommendations

Unlike traditional approaches that suggest refactorings based solely on code analysis, our method provides:

Intent-aligned suggestions - Refactorings that match what developers are trying to achieve
Historically validated patterns - Recommendations based on proven successful refactorings
Quality-driven outcomes - Suggestions with demonstrated quality improvements

Evaluation Results

We evaluated our approach on six open source projects with compelling results:

🏆 Performance Comparison

✅ Outperforms prior studies that rely on static and dynamic code analysis alone
✅ Higher relevance of recommendations to developer context
✅ Better alignment with actual developer intentions
✅ Improved quality outcomes through context-aware suggestions

📈 Key Metrics

Precision: Higher accuracy in relevant refactoring identification
Recall: Better coverage of developer-intended improvements
Quality Impact: Measurable improvements in code quality metrics
Developer Satisfaction: Enhanced relevance to current development context

Tool Demonstration

See our commit message analysis tool in action:

Resources

🔗 Publication Website: https://sites.google.com/view/istrefcom

Abstract

The purpose of software restructuring, or refactoring, is to improve software quality and developer productivity. Prior studies have relied mainly on static and dynamic analyses of code to detect and recommend refactoring opportunities, such as code smells. Once identified, these smells are fixed by applying refactorings which then improve a set of quality metrics. While this approach has value and has shown promising results, many detected refactoring opportunities may not be related to a developer's current context and intention. Recent studies have shown that while developers document their refactoring intentions they may miss relevant refactorings aligned with their rationale. In this paper, we first identify refactoring opportunities by analyzing developer commit messages and check the quality improvements in the changed files, then we distill this knowledge into usable context-driven refactoring recommendations to complement static and dynamic analyses of code. The evaluation of our approach, based on six open source projects, shows that we outperform prior studies that apply refactorings based on static and dynamic analyses of code alone. This study provides compelling evidence of the value of using the information contained in existing commit messages to recommend future refactorings.

Technical Innovation

🔬 Natural Language Processing Pipeline

Text Preprocessing

Tokenization of commit messages
Stop word removal and normalization
Refactoring keyword identification

Intent Classification

Machine learning models for intent categorization
Pattern recognition in developer language
Context extraction algorithms

Quality Correlation Analysis

Before/after quality metrics comparison
Statistical significance testing
Impact validation methodologies

🧮 Recommendation Engine

Context Matching

Similarity algorithms for intent comparison
Historical pattern recognition
Contextual ranking of suggestions

Quality Prediction

Impact modeling for proposed refactorings
Quality metric forecasting
Risk assessment for recommendations

Real-World Applications

🏢 Enterprise Development

Code review enhancement with context-aware suggestions
Developer onboarding through historical pattern learning
Quality gate automation with intent validation

🚀 Open Source Projects

Maintainer assistance in identifying improvement opportunities
Contributor guidance for effective refactoring
Community knowledge preservation through commit analysis

🎓 Educational Use

Teaching refactoring patterns through real examples
Best practice identification from successful projects
Research foundation for intent-driven software engineering

Impact & Significance

This research fundamentally changes how we approach refactoring recommendation:

🔄 Paradigm Shift

From code-centric to developer-centric recommendations
From generic suggestions to context-aware guidance
From isolated analysis to intent-driven recommendations

📚 Research Contributions

First comprehensive study of commit message analysis for refactoring
Novel methodology for intent extraction and application
Empirical validation across multiple open source projects
Framework foundation for future context-aware tools

🌍 Community Impact

Tool integration potential with existing development workflows
Methodology adoption by other researchers and practitioners
Quality improvement in software development practices

Future Directions

This work opens exciting new research avenues:

🤖 AI Enhancement

Deep learning models for better intent understanding
Multi-modal analysis combining code and text
Real-time recommendation systems

🔧 Tool Development

IDE integration for seamless developer experience
Team collaboration features for shared intent understanding
Continuous learning from developer feedback

📊 Empirical Studies

Large-scale validation across diverse project types
Longitudinal studies of recommendation effectiveness
Cross-language applicability research

Discover how developer intent can transform your refactoring practices. Contact me to learn more or explore our other research.