In modern QA and DevOps pipelines, software complexity and release velocity are increasing rapidly. Traditional manual approaches to test planning often miss critical risk areas, resulting in late bug discovery, higher costs, and compromised reliability.
Mission-critical sectors—aviation, healthcare, banking, and government systems—cannot afford such inefficiencies. Early bug prediction is not just a quality issue, but a compliance, security, and safety imperative.
Research Gap: While test execution frameworks exist, very few integrate AI-powered predictive analytics to proactively highlight high-risk modules before defects escape into production.
Thesis: By applying statistical and machine learning techniques to historical QA data, bug prediction can become a first-class citizen in CI/CD pipelines, reducing time-to-detect, improving prioritization, and enabling proactive QA planning.
AutoBugPredictX is an open-source AI-powered bug prediction engine that leverages historical QA execution data to forecast the likelihood of defects in test modules. It provides:
The framework empowers QA teams to focus on high-risk modules, improving testing ROI and reducing critical defect leakage.
Problem: QA teams spend significant time testing modules uniformly, without risk-based prioritization. This leads to wasted effort on low-risk components and missed detection in critical areas.
Solution (AutoBugPredictX):
+----------------------------+
| Historical QA Data |
+----------------------------+
|
+-----------v-----------+
| Feature Engineering |
| (code changes, tests) |
+-----------+-----------+
|
+-----------v-----------+
| ML Model (LogReg) |
| Predict bug likelihood|
+-----------+-----------+
|
+---------------------v----------------------+
| Streamlit Dashboard (UI/UX) |
+---------------------+----------------------+
|
+-----------v-----------+
| Risk Heatmap & Report |
+-----------------------+
module_name, no_of_test_cases, code_changes, past_bugs, etc.).We propose a repeatable evaluation protocol to compare AutoBugPredictX against baseline manual QA prioritization.
Prediction Accuracy (ACC)
Formula (plain text for GitHub):
ACC = (Correct Predictions ÷ Total Predictions) × 100%
LaTeX (for MathJax/Overleaf-supported renderers):
Precision (P)
Where TP = true positives (correctly predicted buggy modules), FP = false positives.
Recall (R)
Where FN = false negatives (missed buggy modules).
F1-Score
Balances precision vs recall for QA prioritization.
AUC-ROC Score
Time-to-Insight (TTI)
National Relevance: AutoBugPredictX contributes to software quality resilience, aligning with national goals for secure digital infrastructure and AI-assisted QA.
GitHub Actions:
Jenkins:
sample_test_logs.csv for evaluation.