A Survey of Self-Admitted Technical Debt Detection

Self-Admitted Technical Debt is a key research area in the current software engineering field. By detecting Self-Admitted Technical Debt, potential bugs in software code can be detected early, thus improving software quality. We have systematically organized and analyzed SATD detection in recent years and proposed several future research directions.

Authors

Xianzhen Dou
Yubin Qu (ORCID: https://orcid.org/0000-0001-5222-4020)
Long Li (ORCID: https://orcid.org/0000-0002-7471-604X)

Publication Details

Journal: 網際網路技術學刊
Published: 2025-05-26
DOI: https://doi.org/10.70003/160792642025052603010
Primary Topic: Financial Distress and Bankruptcy Prediction
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

A Survey of Self-Admitted Technical Debt Detection

Xianzhen Dou, Yubin Qu, Long Li

May 26, 2025

網際網路技術學刊

Financial Distress and Bankruptcy Prediction

article

A Survey of Self-Admitted Technical Debt Detection

Xianzhen Dou, Yubin Qu, Long Li

May 26, 2025article en

Abstract

網際網路技術學刊Vol. 26(3)

Openalex Percentile: Top 84%

Financial Distress and Bankruptcy Prediction

DOI Source OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Survey of Self-Admitted Technical Debt Detection — Xianzhen Dou, Yubin Qu, et al. · 網際網路技術學刊 (2025) | TGRS Research Map | TGRS