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arxiv: 2605.16544 · v1 · pith:QHCRUC5Enew · submitted 2026-05-15 · 💻 cs.SE · cs.GR· cs.HC

TARIPlay: A Test Framework for AR Applications based on Interactive Area Tracking in Playback Videos

Seyed Amir Mousavi , Xiaoyin Wang This is my paper

Pith reviewed 2026-05-20 16:07 UTC · model grok-4.3

classification 💻 cs.SE cs.GRcs.HC
keywords AR testingplayback videosautomated testinginteractive area trackingtest coverageAR applicationsbranch coveragevideo analysis
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The pith

TARIPlay identifies stable and visible interactive areas in AR playback videos to guide automated tests achieving higher branch coverage than Monkey.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces TARIPlay as a framework that analyzes playback videos of AR apps to detect, track, and filter interactive areas using stability and visibility criteria. This addresses the core difficulty that AR interfaces come from volatile real-world environments rather than fixed layouts, so recorded videos must supply both timing and location data for tests. The system then supplies these filtered areas to an automated testing engine that simulates taps and other inputs at the right moments. A sympathetic reader cares because reliable automated testing for AR could improve quality and safety without requiring constant live sessions in changing physical spaces. Evaluation on four open-source apps and nine videos shows 55.8 percent branch coverage of AR-related code compared with 41.98 percent from the existing Monkey tool.

Core claim

TARIPlay analyzes playback videos to detect, track, and filter proper interactive areas over time for automated testing. In particular, TARIPlay identifies viable test opportunities based on criteria like stability and visibility, then feeds this information to an automated testing engine to simulate user interactions. Evaluation results with four open-source AR apps and nine playback videos show that TARIPlay significantly outperforms the existing tool Monkey in test coverage of AR-related code, achieving 55.8 percent branch coverage versus 41.98 percent, and can also be used to assess the quality of playback videos for testing suitability.

What carries the argument

Interactive area tracking mechanism that applies stability and visibility criteria to frames in playback videos to identify and follow dynamic, irregular test opportunities for input simulation.

If this is right

  • Automated testing engines receive timed and located inputs from video analysis instead of attempting to guess dynamic AR surfaces.
  • Playback videos can be scored for testing value by counting how many stable and visible interactive areas they contain.
  • AR apps can reuse real-world recordings for repeated test runs without re-executing the physical scenario each time.
  • Branch coverage on AR-specific code improves because tests focus on environment-derived interaction points rather than generic random inputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same video-based tracking could support testing of other apps whose interfaces depend on sensor data or external scenes.
  • Developers might first run the framework on candidate recordings to decide which real-world captures are worth keeping for regression suites.
  • Combining the area tracker with existing GUI testing tools could extend coverage to mixed AR and traditional screen elements.
  • If the stability criteria hold across varied lighting and motion, the method could reduce the total number of live recordings needed during development.

Load-bearing premise

Stability and visibility criteria applied to playback video frames will reliably identify interactive areas that correspond to actual user-testable opportunities in live AR sessions.

What would settle it

Applying TARIPlay to a new set of AR playback videos where the detected areas miss major user interactions that occur in the corresponding live sessions, yielding branch coverage no better than Monkey.

Figures

Figures reproduced from arXiv: 2605.16544 by Seyed Amir Mousavi, Xiaoyin Wang.

Figure 1
Figure 1. Figure 1: Dynamics of Planes in 2 seconds not be proper interactive areas for testing (and human users typi￾cally do not interact with them). To illustrate the two challenges, in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of Steps in Sutherland-Hodgman Algo [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of TARIPlay Android Y-axis correction: 𝑥screen = (𝑥ndc + 1) 2 ·𝑊 𝑦screen =  1 − 𝑦𝑛𝑑𝑐 + 1 2  · 𝐻 where 𝑊 and 𝐻 are screen dimensions, and (𝑥ndc, 𝑦ndc) = (𝑥screen/𝑤screen, 𝑦screen/𝑤screen). After all vertices of a track￾able polygon are projected to the phone screen plane, we can connect them to form the projection area. • Polygon Clipping of Projection Areas: after projected areas are calculated,… view at source ↗
Figure 5
Figure 5. Figure 5: A Exemplar Gantt Chart Output of TARIPlay shorter than two seconds and report the remaining as test oppor￾tunities. We choose two seconds as the default threshold because it takes about 0.6 seconds for a human being to reflect on a visual stimulus and give hand response [10] [37], so two is the fewest number of whole second that allows two consecutive UI events to be triggered naturally. In addition, the s… view at source ↗
Figure 4
Figure 4. Figure 4: Visible Box Intersection in Life Span Analysis [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Coverage Comparison on Interaction Code happen. Our performance varies across videos because some videos do not have enough test opportunities for complicated gestures (e.g., dragging for precise movements) supported in the app. 4.4.3 RQ2: Coverage vs Complexity. Results in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Correlation Between Coverage and Video Factors, [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

As Augmented Reality (AR) becomes more and more embedded in daily life, ensuring the quality, safety, and reliability of AR applications is increasingly important. However, AR apps present unique challenges for automated testing. Unlike static GUI layouts in traditional mobile apps, AR apps acquire their interaction interface from the surrounding environment, which is volatile and non-deterministic. Recent advancements like ARCore Playback and ARKit Replay allow developers to reuse real-world scenarios by recording and playing back enriched videos, enabling more feasible automated AR testing. However, using playback videos introduces two major challenges: test inputs must be timed precisely, and interactive areas in the video are dynamic, irregular, and difficult to identify. To address these challenges, we propose TARIPlay, a framework that analyzes playback videos to detect, track, and filter proper interactive areas over time for automated testing. In particular, TARIPlay identifies viable test opportunities based on criteria like stability and visibility, then feeds this information to an automated testing engine to simulate user interactions. We perform an experiment with four open-source AR apps and nine playback videos. Evaluation results show that TARIPlay significantly outperforms the existing tool Monkey in test coverage (55.8% over 41.98% on branch coverage) of AR-related code, and can also be used to assess the quality of playback videos for testing suitability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces TARIPlay, a framework for testing AR applications using playback videos. It detects and tracks interactive areas in videos by applying stability and visibility criteria to identify viable test opportunities, which are then used by an automated testing engine to simulate user interactions. Experiments with four open-source AR apps and nine playback videos demonstrate that TARIPlay achieves higher branch coverage (55.8%) of AR-related code compared to the Monkey tool (41.98%).

Significance. If the central claim holds after addressing validation gaps, this could meaningfully advance automated testing for AR apps by leveraging playback features from ARCore and ARKit to handle dynamic interfaces. The use of external open-source apps as benchmarks supports reproducibility. The reported coverage numbers provide a concrete basis for comparison, though the lack of live-session grounding limits immediate impact.

major comments (2)
  1. [Evaluation] Evaluation section: The abstract and results report concrete branch coverage (55.8% vs. 41.98%) but supply no details on how coverage was measured for AR-related code, which code was isolated, how the nine videos were selected, or any statistical tests. This is load-bearing for the outperformance claim over Monkey.
  2. [Approach] Approach section: The core step filters tracked areas using stability and visibility criteria on playback video frames to identify 'viable test opportunities.' No ground-truth comparison to live AR sessions, user-study alignment, or replay validation is described to confirm these areas match actual user-testable interactions in volatile, sensor-driven environments.
minor comments (2)
  1. [Approach] Clarify the precise definitions and thresholds for stability and visibility (e.g., via pseudocode or parameter values) to improve reproducibility.
  2. [Abstract] The abstract claims 'significantly outperforms' without effect sizes or variance measures; add these in the evaluation for precision.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on our manuscript. We have addressed each of the major comments below and will incorporate revisions where appropriate to improve the clarity and rigor of the paper.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: The abstract and results report concrete branch coverage (55.8% vs. 41.98%) but supply no details on how coverage was measured for AR-related code, which code was isolated, how the nine videos were selected, or any statistical tests. This is load-bearing for the outperformance claim over Monkey.

    Authors: We agree with the referee that more methodological details are necessary to substantiate the reported coverage improvements. In the revised manuscript, we will add a dedicated subsection in the Evaluation section detailing: the coverage measurement tool and process (using code coverage frameworks compatible with Android AR apps), the specific isolation of AR-related code by identifying packages and classes that directly interface with ARCore APIs, the rationale and criteria for selecting the nine playback videos to represent a variety of real-world AR scenarios, and the application of statistical tests such as Wilcoxon signed-rank test to assess the significance of the 55.8% vs. 41.98% difference. This will strengthen the evaluation claims. revision: yes

  2. Referee: [Approach] Approach section: The core step filters tracked areas using stability and visibility criteria on playback video frames to identify 'viable test opportunities.' No ground-truth comparison to live AR sessions, user-study alignment, or replay validation is described to confirm these areas match actual user-testable interactions in volatile, sensor-driven environments.

    Authors: We acknowledge that a direct validation against live AR sessions is not provided in the current manuscript. The playback videos are derived from real AR sessions recorded with ARCore, preserving the sensor-driven dynamics. The stability criterion ensures areas persist across frames despite minor movements, and visibility ensures they are not occluded, which are key properties for interactive areas in AR. We will revise the Approach section to include a more detailed justification of these criteria based on AR literature and add a limitations paragraph discussing the challenges of live vs. playback validation due to environmental volatility. Future work could involve user studies for alignment. revision: partial

Circularity Check

0 steps flagged

No significant circularity in TARIPlay evaluation

full rationale

The paper describes an empirical testing framework that applies stability and visibility filters to tracked areas in playback videos, then evaluates the resulting test coverage on four independent open-source AR applications using nine playback videos. The central result (55.8% branch coverage versus Monkey's 41.98%) is obtained by direct execution and measurement against external benchmarks rather than any fitted parameter, self-referential definition, or derivation that reduces to the method's own inputs. No equations, uniqueness theorems, or ansatzes appear in the provided description, and the evaluation chain remains self-contained against the chosen open-source apps and deterministic recordings.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that recorded playback videos faithfully capture the timing and spatial properties needed for realistic AR testing; no free parameters or invented entities are mentioned.

axioms (1)
  • domain assumption ARCore Playback and ARKit Replay videos provide sufficiently accurate and reusable representations of real-world AR interaction scenarios
    Invoked when the paper states that these recordings enable more feasible automated AR testing.

pith-pipeline@v0.9.0 · 5778 in / 1253 out tokens · 49329 ms · 2026-05-20T16:07:25.764159+00:00 · methodology

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