Context: Technical debt (TD) is a well-known metaphor for the long-term
effects of architectural decisions in software development and the trade-off
between producing high-quality, effective, and efficient code and meeting a
release schedule. Thus, the code degrades and needs refactoring. A lack of
resources, time, knowledge, or experience on the development team might cause
TD in any software development project. Objective: In the context of TD
detection, NLP has been utilized to identify the presence of TD automatically
and even recognize specific types of TD. However, the enormous variety of
feature extraction approaches and ML/DL algorithms employed in the literature
often hinders researchers from trying to improve their performance. Method: In
light of this, this SLR proposes a taxonomy of feature extraction techniques
and algorithms used in technical debt detection: its objective is to compare
and benchmark their performance in the examined studies. Results: We selected
55 articles that passed the quality evaluation of this SLR. We then
investigated which feature extractions and algorithms were employed to identify
TD in each SDLC phase. All approaches proposed in the analyzed studies were
grouped into NLP, NLP+ML, and NLP+DL. This allows us to discuss the performance
in three different ways. Conclusion: Overall, the NLP+DL group consistently
outperforms in precision and F1-score for all projects, and in all but one
project for the recall metric. Regarding the feature extraction techniques, the
PTWE consistently achieves higher precision, recall, and F1-score for each
project analyzed. Furthermore, TD types have been mapped, when possible, to
SDLC phases: this served to determine the best-performing feature extractions
and algorithms for each SDLC phase. Finally, based on the SLR results, we also
identify implications that could be of concern to researchers and
practitioners.

软件开发项目中技术债务检测的特征提取技术和算法进行了综述，发现 NLP+DL 组在所有项目中的精确度和 F1 分数上始终表现优秀，并且在召回率度量上几乎所有项目都优于其他组。此外，PTWE 在每个项目的精确度、召回率和 F1 分数上均表现较优，根据综述结果，我们确定了每个软件开发生命周期阶段最佳的特征提取技术和算法，并提出了对研究人员和实践者可能关注的问题。

利用自然语言处理方法检测技术债务 -- 一项系统文献综述

Detecting Technical Debt Using Natural Language Processing Approaches --  A Systematic Literature Review

Overhead line inspection greatly benefits from defect recognition using
visible light imagery. Addressing the limitations of existing feature
extraction techniques and the heavy data dependency of deep learning
approaches, this paper introduces a novel defect recognition framework. This is
built on the Faster RCNN network and complemented by unsupervised semantic
segmentation. The approach involves identifying the type and location of the
target equipment, utilizing semantic segmentation to differentiate between the
device and its backdrop, and finally employing similarity measures and logical
rules to categorize the type of defect. Experimental results indicate that this
methodology focuses more on the equipment rather than the defects when
identifying issues in overhead lines. This leads to a notable enhancement in
accuracy and exhibits impressive adaptability. Thus, offering a fresh
perspective for automating the inspection of distribution network equipment.

该研究提出了一种基于 Faster RCNN 网络和无监督语义分割的新型缺陷识别框架，通过识别目标设备的类型和位置、利用语义分割区分设备和背景，并运用相似度度量和逻辑规则对缺陷进行分类，从而提高了空中线路中设备问题的准确性和适应性，为配电网络设备检测的自动化提供了新的视角。

基于无监督语义分割的架空线缺陷识别

Overhead Line Defect Recognition Based on Unsupervised Semantic  Segmentation

In recent years, the increase in non-Windows malware threats had turned the
focus of the cybersecurity community. Research works on hunting Windows
PE-based malwares are maturing, whereas the developments on Linux malware
threat hunting are relatively scarce. With the advent of the Internet of Things
(IoT) era, smart devices that are getting integrated into human life have
become a hackers highway for their malicious activities. The IoT devices employ
various Unix-based architectures that follow ELF (Executable and Linkable
Format) as their standard binary file specification. This study aims at
providing a comprehensive survey on the latest developments in
cross-architectural IoT malware detection and classification approaches. Aided
by a modern taxonomy, we discuss the feature representations, feature
extraction techniques, and machine learning models employed in the surveyed
works. We further provide more insights on the practical challenges involved in
cross-architectural IoT malware threat hunting and discuss various avenues to
instill potential future research.

本文旨在提供跨架构物联网恶意软件检测和分类方法的最新研究。通过现代分类法，讨论特征表示、特征提取技术和机器学习模型，进一步探讨跨架构物联网恶意软件威胁猎捕中的实际挑战和未来研究的各个途径。