The DOI matches its Crossref record; the references it cites are not checked yet.
Knowledge Graphing Git Repositories: A Preliminary Study
Abstract
Knowledge Graph, being able to connect information from a variety of sources, has become very famous in recent years since its creation in 2012 by Google. Researchers in our community have leveraged Knowledge Graph to achieve various purposes such as improving API caveats accessibilities, generating answers to developer questions, and reasoning common software weaknesses, etc. In this work, we would like to leverage the knowledge graph concept for helping developers and project managers to comprehend software repositories. To this end, we design and implement a prototype tool called GitGraph, which takes as input a Git repository and constructs automatically a knowledge graph associated with the repository. Our preliminary experimental results show that GitGraph can correctly generate knowledge graphs for Git projects and the generated graphs are also useful for users to comprehend the projects. More specifically, the knowledge graph, on one hand, provides a graphic interface that users can interactively explore the integrated artefacts such as commits and changed methods, while on the other hand, provides a convenient means for users to search for advanced relations between the different artefacts.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
The citation lists haven’t been fetched yet
References 7 · Cited by 14
Research works printed but not linked: 21
- 2018, Material
- 2018, NetWork Monitor
- 2018, Padland
- 2018, SuntimesWidge
- 2018, Uber Rides Android SDK
- 10.1145/3038912.3052583 · DOI
- 10.1109/ICDE.2017.228 · DOI
- 10.1109/ICSME.2018.00028 · DOI
- meng, 2017, Androvault: Constructing knowledge graph from millions of android apps for automated computing, arXiv preprint arXiv 1711 03890
- 10.1145/3038912.3052558 · DOI · Paper page
- 2011, Bookworm
- 0, GitVis3D
- 2018, AFWAL
- 2016, Yahoonewsonboarding
- 2018, Flavor
- 2018, Calendula
- 0, GitUp
- 0, Gitential
- 2018, Kolab Notes
- zhang, 2017, Variational reasoning for question answering with knowledge graph, arXiv preprint arXiv 1709 04396
- 10.1109/SANER.2017.7884609 · DOI
Metadata
- Authors
- Zhao, Y., Wang, H., Ma, L., Liu, Y., Li, L., & Grundy, J.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Harbin Institute of Technology (CN)
- Monash University (AU)
- Venue
- 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 599-603
- OpenAlex
- W2921805009
- Cited by
- 14
- References
- 7
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 2 · 2021: 3 · 2020: 2
- FWCI
- 0.98
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- knowledge graphs, Git repositories, software repositories, commit analysis, software artefacts, software engineering tools
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
