MaryAngel/AdaptCI-XAI

AdaptCI-XAI: CI Pipeline Failure Classifier

Research project: IS 8101 — Sabaragamuwa University of Sri Lanka Student: Mary Angel Anton Premathas (20APC4548) Paper: AdaptCI-XAI: Explainable AI for CI Pipeline Failure Diagnosis using Transformer-Based Models on GitHub Actions

What this model does

Classifies GitHub Actions CI/CD pipeline failure logs into 4 categories: | Label | Description | |-------|-------------| | config_error | Malformed YAML, outdated action versions | | dependency_failure | npm/pip install failures, missing packages | | test_failure | Unit/integration test failures, type errors | | infrastructure | Runner timeout, OOM, network errors |

How to use

from transformers import pipeline
clf = pipeline("text-classification", model="MaryAngel/AdaptCI-XAI")
result = clf("npm ERR ENOENT no such file or directory node_modules/react")
print(result)
# [{'label': 'dependency_failure', 'score': 0.94}]

Training details

• —Base model: bert-base-uncased
• —Fine-tuning: LoRA (r=8, alpha=16, target=query+value layers)
• —Trainable parameters: ~0.54% of BERT total
• —Training data: Real failed CI runs from facebook/react (GitHub API)
• —Hardware: Google Colab T4 GPU (free tier)
• —Weighted F1: 0.7124

Novelty

1. 1.First LoRA fine-tuning applied to CI/CD log classification
2. 2.First SHAP attribution on CI/CD failure predictions
3. 3.First expertise-aware (novice) adaptive explanation system for CI/CD
4. 4.Multi-source labelling: log text + workflow name + step name signals
5. 5.Fully reproducible on FREE hardware (Colab T4 + HF)

XAI

Each prediction includes SHAP token attribution showing which log words drove the classification decision — making the black-box model transparent to developers.