SoK: Software Debloating Landscape and Future Directions
- URL: http://arxiv.org/abs/2407.11259v1
- Date: Mon, 15 Jul 2024 21:52:21 GMT
- Title: SoK: Software Debloating Landscape and Future Directions
- Authors: Mohannad Alhanahnah, Yazan Boshmaf, Ashish Gehani,
- Abstract summary: We conceptualize the software debloating workflow, which serves as the basis for developing a multilevel taxonomy.
This framework classifies debloating tools according to their input/output artifacts, debloating strategies, and evaluation criteria.
- Score: 3.5609179225884353
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Software debloating seeks to mitigate security risks and improve performance by eliminating unnecessary code. In recent years, a plethora of debloating tools have been developed, creating a dense and varied landscape. Several studies have delved into the literature, focusing on comparative analysis of these tools. To build upon these efforts, this paper presents a comprehensive systematization of knowledge (SoK) of the software debloating landscape. We conceptualize the software debloating workflow, which serves as the basis for developing a multilevel taxonomy. This framework classifies debloating tools according to their input/output artifacts, debloating strategies, and evaluation criteria. Lastly, we apply the taxonomy to pinpoint open problems in the field, which, together with the SoK, provide a foundational reference for researchers aiming to improve software security and efficiency through debloating.
Related papers
- A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models [2.518519330408713]
Large Language Models (LLMs) in software engineering have sparked interest in their use for software vulnerability detection.<n>The rapid development of this field has resulted in a fragmented research landscape.<n>This fragmentation makes it difficult to obtain a clear overview of the state-of-the-art or compare and categorize studies meaningfully.
arXiv Detail & Related papers (2025-07-30T13:17:16Z) - Learning Underwater Active Perception in Simulation [51.205673783866146]
Turbidity can jeopardise the whole mission as it may prevent correct visual documentation of the inspected structures.
Previous works have introduced methods to adapt to turbidity and backscattering.
We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions.
arXiv Detail & Related papers (2025-04-23T06:48:38Z) - A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
Underwater image enhancement (UIE) presents a significant challenge within computer vision research.
Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
arXiv Detail & Related papers (2024-05-30T04:46:40Z) - Code Compass: A Study on the Challenges of Navigating Unfamiliar Codebases [2.808331566391181]
We propose a novel tool, Code, to address these issues.
Our study highlights a significant gap in current tools and methodologies.
Our formative study demonstrates how effectively the tool reduces the time developers spend navigating documentation.
arXiv Detail & Related papers (2024-05-10T06:58:31Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - A Broad Comparative Evaluation of Software Debloating Tools [3.0913520619484287]
Software debloating tools seek to improve program security and performance by removing unnecessary code, called bloat.
We surveyed 10 years of debloating literature and several tools currently under commercial development to taxonomize knowledge about the debloating ecosystem.
Our evaluation, conducted on a diverse set of 20 benchmark programs, measures tools across 12 performance, security, and correctness metrics.
arXiv Detail & Related papers (2023-12-20T18:53:18Z) - Jup2Kub: algorithms and a system to translate a Jupyter Notebook
pipeline to a fault tolerant distributed Kubernetes deployment [0.9790236766474201]
Scientific facilitate computational, data manipulation, and sometimes visualization steps for scientific data analysis.
Jupyter notebooks struggle to scale with larger data sets, lack failure tolerance, and depend heavily on the stability of underlying tools and packages.
Jup2Kup translates from Jupyter notebooks into a distributed, high-performance environment, enhancing fault tolerance.
arXiv Detail & Related papers (2023-11-21T02:54:06Z) - Using Machine Learning To Identify Software Weaknesses From Software
Requirement Specifications [49.1574468325115]
This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications.
Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naive Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested.
arXiv Detail & Related papers (2023-08-10T13:19:10Z) - Dataflow graphs as complete causal graphs [17.15640410609126]
We consider an alternative approach to software design, flow-based programming (FBP)
We show how this connection can be leveraged to improve day-to-day tasks in software projects.
arXiv Detail & Related papers (2023-03-16T17:59:13Z) - A modular software framework for the design and implementation of
ptychography algorithms [55.41644538483948]
We present SciCom, a new ptychography software framework aiming at simulating ptychography datasets and testing state-of-the-art reconstruction algorithms.
Despite its simplicity, the software leverages accelerated processing through the PyTorch interface.
Results are shown on both synthetic and real datasets.
arXiv Detail & Related papers (2022-05-06T16:32:37Z) - Satellite Image Time Series Analysis for Big Earth Observation Data [50.591267188664666]
This paper describes sits, an open-source R package for satellite image time series analysis using machine learning.
We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome.
arXiv Detail & Related papers (2022-04-24T15:23:25Z) - Geometric Optimisation on Manifolds with Applications to Deep Learning [6.85316573653194]
We design and implement a Python library to help the non-expert using all these powerful tools.
The algorithms implemented in this library have been designed with usability and GPU efficiency in mind.
arXiv Detail & Related papers (2022-03-09T15:20:07Z) - Software Vulnerability Detection via Deep Learning over Disaggregated
Code Graph Representation [57.92972327649165]
This work explores a deep learning approach to automatically learn the insecure patterns from code corpora.
Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
arXiv Detail & Related papers (2021-09-07T21:24:36Z)
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.