Enterprise AI Analysis
Unlock Transparency in ML/DL for Software Engineering
Our comprehensive systematic literature review dissects 108 primary studies, revealing critical insights into explainable AI (XAI) applications in Software Engineering (SE) from 2012-2024. Understand the current landscape, identify challenges, and discover future opportunities.
Executive Impact Summary
Key findings from our analysis highlight the rapid evolution and strategic importance of Explainable AI in diverse Software Engineering domains.
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Primary Studies Analyzed
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Unique SE Tasks Covered
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Major SDLC Activities
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Out-of-the-Box Toolkit Usage
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overview
RQ1: SE Tasks
RQ2: XAI Techniques
RQ3: Evaluation
Research Overview
This systematic literature review provides a comprehensive overview of Explainable AI (XAI) techniques applied to Machine Learning (ML) and Deep Learning (DL) models within Software Engineering (SE). We analyzed 108 primary studies published between 2012 and 2024, identifying key trends, challenges, and opportunities.
The study highlights the critical need for explainability in SE, especially for high-stakes applications like vulnerability detection, where transparency in decision-making is crucial. Our findings categorize SE tasks, classify XAI techniques, and investigate existing evaluation approaches, providing guidelines for future research.
RQ1: Types of AI4SE Studies for Explainability
Our analysis categorized primary studies into 23 unique SE tasks across five major software development life cycle activities. Early research focused heavily on traditional classification tasks such as Bug/Defect Prediction. However, since 2021, there's been a shift towards more complex SE tasks, including Developer Recommendation, Root Cause Analysis, and Program Synthesis.
While software maintenance (49.1%) and testing (24.1%) received the most attention, areas like software requirements & design (0.9%) and software management (3.7%) remain significantly underrepresented, suggesting promising avenues for future XAI research in SE.
RQ2: How XAI Techniques Support SE Tasks
Existing XAI techniques for SE tasks are primarily developed along five directions: Out-of-the-Box Toolkit (34%), Interpretable Model (23%), Domain Knowledge (20%), Attention Mechanism (10%), and highly tailored approaches (13%).
The selection of these techniques is guided by Task Fitness, Model Compatibility, and Stakeholder Preference. We observed a variety of explanation formats, including Numeric (27%), Text (23%), Visualization (9%), Source Code (20%), and Rule (20%), often tightly associated with specific SE tasks and stakeholder needs.
RQ3: XAI Performance & Evaluation
Our findings highlight a notable scarcity of well-documented and reusable baselines or benchmarks for XAI4SE. Approximately 28.7% of benchmarks were self-generated and often not publicly accessible. This lack of standardization makes comparative analysis challenging.
There is no widespread consensus on evaluation strategies, with assessments often relying on specific properties like correctness and coherence, or even researchers' subjective intuition. This underscores a critical need for standardized protocols and publicly available resources for robust evaluation.
Unprecedented Growth in XAI4SE Research
108 Primary Studies Analyzed (2012-2024)The field of Explainable AI for Software Engineering (XAI4SE) has seen remarkable growth, with a significant number of studies focusing on key areas.
Comparison of Our Work with Previous Surveys
Our systematic literature review expands significantly on prior work by covering a broader scope, more papers, and a custom taxonomy tailored for SE.
| Feature | Mohammadkhani et al. [85] | Yang et al. [155] | Our Survey |
|---|---|---|---|
| Studied Model | ML & DL | CodeLMs | ML & DL (+LLM) |
| Scope | -2022 | 2019-2023 | 2012-2024 |
| # Papers | 24 | 146 (16) | 108 |
| Taxonomy | General | General | Customized |
| Evaluation Baseline | † | ○ | ● |
| Evaluation Benchmark | † | ○ | ● |
| Evaluation Metric | † | ○ | ● |
| Guideline | ○ | ○ | ● |
XAI for SE Guidelines Checklist
A structured approach for applying XAI in Software Engineering research, ensuring comprehensive consideration of all critical aspects.
Guideline 1: Requirement Analysis
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Guideline 2: Approach Selection
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Guideline 3: Multi-Dimensional Evaluation
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Guideline 4: Feedback-Driven Optimization
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Guideline 5: Legal and Ethical Considerations
Impact of Explainable AI in Vulnerability Detection
Explainable AI techniques significantly enhance vulnerability detection by providing insights into model decisions, allowing developers to efficiently analyze and fix security flaws. This transparency builds trust and facilitates faster remediation.
For instance, Li et al. [65] leveraged GNNExplainer to simplify PDG sub-graphs, highlighting crucial statements. Zhou et al. [175] provided explanatory descriptions to help analysts understand vulnerability types and root causes. These applications demonstrate XAI's critical role in high-stakes security tasks, moving beyond black-box predictions to actionable insights.
Advanced ROI Calculator
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Your Strategic Implementation Roadmap
A phased approach to successfully integrate Explainable AI into your enterprise, maximizing transparency and impact.
Discovery & Strategy
Initial assessment of current AI/ML implementations, identification of key SE tasks for XAI integration, and strategic planning based on business objectives.
XAI Approach Selection
Evaluating and selecting the most suitable XAI techniques (Out-of-the-Box, Interpretable Models, Domain Knowledge, etc.) based on task fitness, model compatibility, and stakeholder preferences.
Pilot Implementation & Evaluation
Deploying selected XAI techniques in a pilot project. Conducting multi-dimensional evaluation using quantitative and qualitative metrics to assess correctness, consistency, and coherence.
Feedback & Optimization
Gathering user feedback through interviews and observations. Iteratively refining XAI explanations and model integration to improve usability and effectiveness.
Scalable Deployment & Monitoring
Full-scale deployment of XAI-enabled SE solutions. Continuous monitoring for performance, ethical considerations, and compliance with legal frameworks.
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