Expense - Negative expense does not appear in unreported expense list in workspace report

[lanitochka17]

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Version Number: 9.2.35-0
Reproducible in staging?: Y
Reproducible in production?: N
If this was caught during regression testing, add the test name, ID and link from TestRail: #72552
Email or phone of affected tester (no customers): jsdonisodjsosjos11@gmail.com
Issue reported by: Applause - Internal Team

Action Performed:

Precondition:

• Account has self DM and workspace

1. Go to staging.new.expensify.com
2. Go to workspace chat
3. Create an empty report
4. Go to self DM
5. Create an expense with negative amount
6. Open the expense
7. Click Report
8. Select the empty report from Step 3
   → Negative expense can be added to workspace report
9. Go to self DM
10. Create an expense with negative amount
11. Go to workspace chat
12. Create an empty report
13. On the empty report, click Add expense > Add unreported expense

Expected Result:

Negative expense should appear in the list of unreported expense when it is opened from workspace report, because it can be added to workspace report from Report field without error (Step 8)

Actual Result:

Negative expense does not appear in the list of unreported expense when it is opened from workspace report

Workaround:

Unknown

Platforms:

Select the officially supported platforms where the issue was reproduced:

• Android: App
• Android: mWeb Chrome
• iOS: App
• iOS: mWeb Safari
• iOS: mWeb Chrome
• Windows: Chrome
• MacOS: Chrome / Safari
• MacOS: Desktop

Platforms Tested:

On which of our officially supported platforms was this issue tested:

• Android: App
• Android: mWeb Chrome
• iOS: App
• iOS: mWeb Safari
• iOS: mWeb Chrome
• Windows: Chrome
• MacOS: Chrome / Safari
• MacOS: Desktop

Screenshots/Videos

Add any screenshot/video evidence

Bug6980690_1761059510417.Screen_Recording_2025-10-21_at_23.09.10.mp4

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[melvin-bot]

Triggered auto assignment to @clarajones-expensify (Bug), see https://stackoverflow.com/c/expensify/questions/14418 for more details. Please add this bug to a GH project, as outlined in the SO.

[melvin-bot]

Triggered auto assignment to @stitesExpensify (DeployBlockerCash), see https://stackoverflowteams.com/c/expensify/questions/9980/ for more details.

[melvin-bot]

💬 A slack conversation has been started in #expensify-open-source

[github-actions]

👋 Friendly reminder that deploy blockers are time-sensitive ⏱ issues! Check out the open `StagingDeployCash` deploy checklist to see the list of PRs included in this release, then work quickly to do one of the following:

1. Identify the pull request that introduced this issue and revert it.
2. Find someone who can quickly fix the issue.
3. Fix the issue yourself.

[lanitochka17]

Production:

Recording.4020.mp4

[ikevin127]

🔄 I ran Vector¹ on this deploy blocker issue and here are the top 3 possible offending PRs based on semantic similarity² and reranking:

• #72552: Fix: Don't show negative transactions on Add unreported expense screen
• #72697: Fix/Dont allow merging transactions with negative amount
• #72749: fix: blank row appears after creating a new report field in IOS

Please drop a 👍 (correct) or 👎 (incorrect) as feedback regarding the results (will use to fine-tune the reranking model).

Note

PR # and name added as text only to avoid triggering regression automation on the issues which the above PRs are addressing in case the finding is incorrect.

Footnotes

1. Vector is currently a local-only prototype stage app that uses AI embeddings to find offending PRs for a given deploy blocker issue. Because of the early stage of the app, results must be taken with with a grain of🧂. How it works: grab deploy blocker issue's OP data (title, steps, expected / actual result) and embed -> grab all deploy checklist issue PRs data (title, explanation of change, tests, changed files, AI diff summary) and embed -> calculate cosine similarity of issue embedding to each PRs embedding -> get results -> perform reranking -> get final results. ↩

2. Semantic similarity, when applied to embeddings, measures the similarity in meaning between different pieces of text by comparing their vector representations in a high-dimensional "semantic space". Texts with similar meanings are mapped to vectors that are close together, and this "closeness" is quantified using metrics like cosine similarity. This technology enables search engines and chatbots to find relevant information even without exact keyword matches, providing more accurate and contextualized results. ↩