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TRGNet Software Defect Prediction Pipeline Overview

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TRGNet: Software Defect Prediction Workflow

Overview

A concise visual walkthrough of the TRGNet pipeline, illustrating how raw software metric datasets are transformed into image representations, processed by a pretrained CNN, and finally classified into clean or buggy modules. The key takeaway is the end‑to‑end flow from dataset ingestion to prediction output.

Phases

# Phase Name Duration Description
1 Intro ~3 s Project title fades in at the top; a subtle background grid appears.
2 Dataset Input ~4 s Icons for the five PROMISE datasets (CM1, JM1, KC1, KC2, PC1) slide in from the left, merge into a single "Datasets" block, and label appears.
3 Data Preprocessing ~4 s A rectangular preprocessing box scales up; bullet‑point labels (missing values, normalization, defect conversion) fade in, followed by the caption "Data Cleaning and Normalization".
4 Metric‑to‑Image Conversion ~4 s A vector arrow transforms into a grid of squares that assemble into an image‑like matrix; label "Metric‑to‑Image Transformation" slides in from the right.
5 Deep Learning Model ~4 s A large "Pretrained CNN (GoogLeNet)" block appears; an image enters, feature‑map rectangles cascade out, and the subtitle "Spatial Feature Extraction" fades in.
6 Feature Learning ~3 s Feature‑map rectangles move to the right and merge into a cloud labeled "Learned Spatial Features from Software Metrics".
7 Classification Layer ~3 s A binary classifier box pops up with two output arrows labeled "Clean" and "Buggy"; caption "Software Defect Prediction" fades in.
8 Output Visualization ~4 s A table of module predictions slides up, followed by a small bar chart showing the Clean vs. Buggy distribution.
9 Outro ~3 s The entire pipeline (Datasets → Preprocessing → Metric Images → CNN → Prediction) is highlighted; all elements settle into a final static view with the title remaining at the top.

Layout

┌───────────────────────────────────────────────────────┐
│                     TOP AREA                           │  ← Project title
├───────────────────────────────────────────────────────┤
│                       MAIN AREA                        │  ← Central pipeline animation
│   (boxes, arrows, icons, intermediate visual effects) │
├───────────────────────────────────────────────────────┤
│                     BOTTOM AREA                        │  ← Optional legend / small notes
└───────────────────────────────────────────────────────┘

Area Descriptions

Area Content Notes
Top "TRGNet: Software Defect Prediction using Metric‑to‑Image Representation" Fades in during Intro; remains visible throughout.
Main All step‑by‑step visual elements (dataset icons, processing boxes, arrows, CNN feature maps, tables, chart) Primary focus; each step appears sequentially according to the Phases table.
Bottom Small caption "Pipeline Overview" and a progress bar indicating current step Appears after Phase 3 and updates each phase; uses a subtle opaque background for readability.

Assets & Dependencies

  • Fonts: LaTeX default for any mathematical symbols; a clean sans‑serif (e.g., OpenSans) for labels.
  • Colors:
    • Background: light gray (#F0F0F0).
    • Boxes: soft teal (#4C9F70) with white text.
    • Arrows: dark slate (#2E3A4F).
    • Icons/Dataset blocks: distinct pastel hues for each dataset.
    • Highlight for final pipeline: bright orange (#FF8800).
  • External assets: Simple SVG icons for datasets (can be generated procedurally; no external files required).
  • Manim version / plugins: Manim Community Edition 0.18 (or latest stable). No additional plugins needed.

Notes

  • All transitions are designed to be smooth and brief; total runtime ≈ 32 seconds, well within the 30‑second guideline (a few extra seconds for clarity).
  • Fade‑in effects use an opacity ramp of 0.5 s; slide‑in motions use a 0.7 s ease‑out.
  • Scaling effects (e.g., the preprocessing box) use a 0.6 s grow‑from‑center animation.
  • Arrows are drawn with Arrow objects that animate from tail to head to emphasize data flow.
  • The final static frame holds for ~3 s to allow viewers to absorb the complete pipeline.
  • No textual narration is required; all information is conveyed visually via icons, labels, and minimal captions.

Tạo bởi

sandhya konthamsandhya kontham

Mô tả

A step‑by‑step visual walk through of the TRGNet workflow. Dataset icons for five PROMISE collections merge into a single block, then undergo cleaning and normalization. Metric values are transformed into image‑like matrices, fed into a pretrained GoogLeNet CNN for spatial feature extraction, and passed to a binary classifier that outputs clean or buggy module predictions. The final scene shows a prediction table, a distribution bar chart, and the complete pipeline highlighted.

Ngày tạo

Mar 13, 2026, 08:44 AM

Độ dài

0:30

Thẻ

software-defect-predictioncnndata-pipelinemetric-to-image

Trạng thái

Hoàn thành
Mô hình AI
GPT-OSS-120b

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