Columbus: Filesystem Tree Introspection for Software Discovery
| Title | Columbus: Filesystem Tree Introspection for Software Discovery |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Nadgowda, S., Duri, S., Isci, C., Mann, V. |
| Conference Name | 2017 IEEE International Conference on Cloud Engineering (IC2E) |
| Date Published | apr |
| Keywords | agile development practices, cloud, cloud computing, Columbus, compositionality, data structures, Docker Images, drift detection situations, feature extraction, file metadata, filesystem tree introspection, learning (artificial intelligence), licensing requirement compliance, machine learning methods, meta data, metadata, Metadata Discovery Problem, Operational analytics, Packaging, problem diagnosis, program diagnostics, pubcrawl, querying package management tools, Resiliency, Scalability, software discovery, software engineering, software management, software packages, software packaging knowledge, Standards, Tools |
| Abstract | Software discovery is a key management function to ensure that systems are free of vulnerabilities, comply with licensing requirements, and support advanced search for systems containing given software. Today, software is predominantly discovered through querying package management tools, or using rules that check for file metadata or contents. These approaches are inadequate as not every software is installed through package managers, and agile development practices lead to frequent deployment of software. Other approaches to software discovery use machine learning methods requiring training phase, or require maintaining knowledge bases. Columbus uses the knowledge of the software packaging practices that evolved over time, and uses the information embedded in the file system impression created by a software package to discover it. Columbus is able to discover software in 92% of all official Docker images. Further, Columbus can be used in problem diagnosis and drift detection situations to compare two different systems, or to determine the evolution of a system overtime. |
| DOI | 10.1109/IC2E.2017.14 |
| Citation Key | nadgowda_columbus:_2017 |
- Metadata Discovery Problem
- tools
- standards
- software packaging knowledge
- software packages
- software management
- software engineering
- software discovery
- Scalability
- Resiliency
- querying package management tools
- pubcrawl
- program diagnostics
- problem diagnosis
- Packaging
- Operational analytics
- agile development practices
- metadata
- meta data
- machine learning methods
- licensing requirement compliance
- learning (artificial intelligence)
- filesystem tree introspection
- file metadata
- feature extraction
- drift detection situations
- Docker Images
- data structures
- Compositionality
- Columbus
- Cloud Computing
- cloud