⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

January 1, 2026Open Access

Using Analysis Coupling to Detect Information Flow Security Vulnerabilities

Frederik Reiche Ralf Reussner Robert Heinrich

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim is to identify security vulnerabilities related to information flow in software systems using analysis coupling techniques.
Proposed approach utilizes analysis coupling for vulnerability detection
Publicly available replication package for evaluation
Utilizes techniques from software engineering literature
Demonstrated effectiveness of analysis coupling in identifying vulnerabilities
Evaluation supported by publicly shared replication package

No abstract yet

We only have this paper's title and publication details.

Read it at the source→

Implication

Analysis coupling detects information flow security vulnerabilities in software, highlighting key evaluation methods.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Reiche et al. (2026) studied this question.

synapsesocial.com/papers/698828d90fc35cd7a8848b4d https://doi.org/10.18420/se2026_63

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1An Extensible Framework for Architecture-Based Data Flow Analysis for Information Security2024
2Analysis of information flow security using software implementing business logic based on stored database program blocks2024 · 1 citations
3Accurate Architectural Threat Elicitation From Source Code Through Hybrid Information Flow Analysis2024
4SOFTWARE PROTECTION METHOD BASED ON HYBRID CODE ANALYSIS2025
5Using Information Flow to estimate interference between developers same method contributions2024 · 2 citations

Predictive Analysis of Information Flows in Information Security Systems of Telecommunication Complexes2025
Go With the Flow: Improving the Precision of Static Data Flow Analysis with Context-Sensitive Sinks2026
Uncovering Hidden Dependencies: Constructing Intelligible Path Witnesses using Dataflow Analyses2024