data-flow coverage

Concept

Data-flow coverage is a coverage metric that quantifies validation or testing effectiveness by tracking the flow of data between interconnected functional units, variables, or program elements. In hardware (post-silicon SoC) validation it generalizes functionality by measuring inter-block data streams; in software testing it is expressed over definition-use (def-use) pairs; and in spectrum-based fault localization (SFL) it has been shown to rank more faults in top positions than control-flow spectra at the cost of higher execution overhead.

First seen 6/16/2026

Last seen 6/16/2026

Evidence 2 chunks

Wiki v1

WIKI

Definition

Data-flow coverage is a coverage measurement approach that quantifies how thoroughly a system, test suite, or validation campaign exercises the movement of data between producers and consumers. Rather than counting executed statements, branches, or paths (as in statement, branch, or path coverage), data-flow coverage observes whether the data dependencies implied by the design or program have actually been exercised.

It is therefore closely related to, but distinct from, coverage metrics in general — data-flow coverage is one specific metric within that broader family.

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CITATIONS

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6 citations — click to expand

[1] Pre-silicon code-coverage metrics (statement, branch, path) are not directly applicable in post-silicon, and on-chip coverage logic adds design time and cost. Unified Coverage Methodology for SoC Post-Silicon Validation

[2] A data-flow-based coverage metric generalizes functionality to the flow of data among on-chip functional blocks, and DMA transferring data from memory to a USB device is given as an example. Unified Coverage Methodology for SoC Post-Silicon Validation

[3] The unified coverage methodology is built around data-flow coverage and consists of an On-chip Data Capturing (ODC) unit communicating to an Off-chip Coverage Analyzer (OCA) over JTAG; ODC can be prototyped using the Xilinx ChipscopePro ILA core. Unified Coverage Methodology for SoC Post-Silicon Validation

[4] Further research is needed on coverage metrics relevant to post-silicon to reduce validation cycles, with focus shifting toward ECO validation rather than full end-to-end validation due to shortened market windows. Unified Coverage Methodology for SoC Post-Silicon Validation

[5] Data-flow testing (DFT) targets def-use pairs and is improved by combining symbolic execution (e.g., KLEE) with software model checking to eliminate infeasible test objectives and improve data-flow coverage. Towards Efficient Data-flow Test Data Generation

[6] Using data-flow spectra (DUAs) instead of control-flow spectra in SFL ranks up to 50% more faults in the top-15 positions, with most metrics performing better up to the top-40 positions, at an average execution overhead of 353% (vs 102% for control-flow). Evaluating data-flow coverage in spectrum-based fault localization