DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring
Self-Admitted Technical Debt
- URL: http://arxiv.org/abs/2308.13128v1
- Date: Fri, 25 Aug 2023 01:05:38 GMT
- Title: DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring
Self-Admitted Technical Debt
- Authors: Yikun Li, Mohamed Soliman, Paris Avgeriou, Maarten van Ittersum
- Abstract summary: Technical debt, specifically Self-Admitted Technical Debt (SATD), remains a significant challenge for software developers and managers.
This paper presents DebtViz, an innovative SATD tool designed to automatically detect, classify, visualize and monitor various types of SATD in source code comments and issue tracking systems.
- Score: 1.6201475185215248
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Technical debt, specifically Self-Admitted Technical Debt (SATD), remains a
significant challenge for software developers and managers due to its potential
to adversely affect long-term software maintainability. Although various
approaches exist to identify SATD, tools for its comprehensive management are
notably lacking. This paper presents DebtViz, an innovative SATD tool designed
to automatically detect, classify, visualize and monitor various types of SATD
in source code comments and issue tracking systems. DebtViz employs a
Convolutional Neural Network-based approach for detection and a deconvolution
technique for keyword extraction. The tool is structured into a back-end
service for data collection and pre-processing, a SATD classifier for data
categorization, and a front-end module for user interaction. DebtViz not only
makes the management of SATD more efficient but also provides in-depth insights
into the state of SATD within software systems, fostering informed
decision-making on managing it. The scalability and deployability of DebtViz
also make it a practical tool for both developers and managers in diverse
software development environments. The source code of DebtViz is available at
https://github.com/yikun-li/visdom-satd-management-system and the demo of
DebtViz is at https://youtu.be/QXH6Bj0HQew.
Related papers
- The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach [56.4040698609393]
Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition.
Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies.
We propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings.
arXiv Detail & Related papers (2024-09-10T10:12:37Z) - Designing and Implementing a Generator Framework for a SIMD Abstraction Library [53.84310825081338]
We present TSLGen, a novel end-to-end framework for generating an SIMD abstraction library.
We show that our framework is comparable to existing libraries, and we achieve the same performance results.
arXiv Detail & Related papers (2024-07-26T13:25:38Z) - SATDAUG -- A Balanced and Augmented Dataset for Detecting Self-Admitted
Technical Debt [6.699060157800401]
Self-admitted technical debt (SATD) refers to a form of technical debt in which developers explicitly acknowledge and document the existence of technical shortcuts.
We share the textitSATDAUG dataset, an augmented version of existing SATD datasets, including source code comments, issue tracker, pull requests, and commit messages.
arXiv Detail & Related papers (2024-03-12T14:33:53Z) - Utilization of machine learning for the detection of self-admitted
vulnerabilities [0.0]
Technical debt is a metaphor that describes not-quite-right code introduced for short-term needs.
Developers are aware of it and admit it in source code comments, which is called Self- Admitted Technical Debt (SATD)
arXiv Detail & Related papers (2023-09-27T12:38:12Z) - Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs)
The system is into three inter-connected core processes-interaction, reflection, and reaction.
Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
arXiv Detail & Related papers (2023-09-15T02:42:03Z) - Automatically Estimating the Effort Required to Repay Self-Admitted
Technical Debt [1.8208834479445897]
Self-Admitted Technical Debt (SATD) is a specific form of technical debt documented by developers within software artifacts.
We propose a novel approach for automatically estimating SATD repayment effort, utilizing a comprehensive dataset.
Our findings show that different types of SATD require varying levels of repayment effort, with code/design, requirement, and test debt demanding greater effort compared to non-SATD items.
arXiv Detail & Related papers (2023-09-12T07:40:18Z) - CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal
Reasoning [107.81733977430517]
CausalVLR (Causal Visual-Linguistic Reasoning) is an open-source toolbox containing a rich set of state-of-the-art causal relation discovery and causal inference methods.
These methods have been included in the toolbox with PyTorch implementations under NVIDIA computing system.
arXiv Detail & Related papers (2023-06-30T08:17:38Z) - DiffStack: A Differentiable and Modular Control Stack for Autonomous
Vehicles [75.43355868143209]
We present DiffStack, a differentiable and modular stack for prediction, planning, and control.
Our results on the nuScenes dataset indicate that end-to-end training with DiffStack yields substantial improvements in open-loop and closed-loop planning metrics.
arXiv Detail & Related papers (2022-12-13T09:05:21Z) - Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data
Programming [77.38174112525168]
We present Nemo, an end-to-end interactive Supervision system that improves overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS supervision approach.
arXiv Detail & Related papers (2022-03-02T19:57:32Z) - Identifying Self-Admitted Technical Debt in Issue Tracking Systems using
Machine Learning [3.446864074238136]
Technical debt is a metaphor for sub-optimal solutions implemented for short-term benefits.
Most work on identifying Self-Admitted Technical Debt focuses on source code comments.
We propose and optimize an approach for automatically identifying SATD in issue tracking systems using machine learning.
arXiv Detail & Related papers (2022-02-04T15:15:13Z) - UX Debt: Developers Borrow While Users Pay [2.9479490707938982]
User experience (UX) debt focuses on shortcuts taken to speed up development at the expense of subpar usability.
Most research considers code-centric technical debt, focusing on the implementation.
We outline three classes of UX debt that we observed in practice: code-centric, architecture-centric, and process-centric.
arXiv Detail & Related papers (2021-04-14T14:59:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.