Software can finish a task, return a plausible result, and never show an error—yet still be wrong. A bug is a departure from intended behavior, not merely a crash. As the EPFL HexHive Magma FAQ puts it, “A bug is a diversion from intended program behavior.” To find these failures, check results against facts independent of the program that produced them.
Can software be wrong without throwing an error?
Yes. A crash is only one way a defect becomes visible. A program can keep running while returning the wrong value, leaving out part of a result, or failing to preserve a user’s work. Magma’s bug benchmark explicitly recognizes that a bug’s trigger condition can occur without causing a crash; later code changes can even prevent a crash while the underlying trigger still indicates a bug.
That distinction matters because a successful return, responsive interface, or neatly formatted result is not evidence of correctness by itself. A crash is conspicuous; a plausible but incorrect result needs an independent check.
What do non-crashing bugs look like?
Wrong or inconsistent values
Dates can be ambiguous across conventions. For example, “03/04/2026” can mean March 4 or April 3. In a document parser, choosing an interpretation row by row can produce dates that look valid individually but are inconsistent across the document.
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Missing or incomplete output
A parser may skip a section yet report a successful import. In a case study published on DEV, the author reported one run that extracted four transactions from a statement containing 35, without showing an error to alert the user. This is an individual author-reported example, not a measure of how often parsers fail.
Work that disappears while the app stays open
In a personal engineering account, Abdulkabir Musa describes finding behavior in the Clypra codebase where an application appeared normal while user work was discarded. It illustrates how a lifecycle or state-handling defect can be serious without terminating the process; it is not an independently verified incident report.
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A visible freeze rather than bad data
Not every non-crashing defect is silent to the user. An Android app may stop responding while its process remains alive. That is a different symptom from a parser quietly returning incomplete data, and it calls for platform diagnostics rather than only checking the result.
How do you test plausible but incorrect results?
Compare output with an independent expectation
Look for a fact that exists outside the code path being tested. If a bank statement prints a total, compare the sum of extracted transactions with that printed total. If the figures do not reconcile, the mismatch exposes a problem even when the import reports success. This check is useful when the input provides a trustworthy total, but it cannot catch every error or replace other tests.
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Build tests from known facts
Create fixtures with planted properties and assert those properties directly: known dates, transaction counts, balances, or expected state changes. Avoid simply running the program, observing its current output, and encoding that output as the expected answer; if the implementation is already wrong, that approach can turn the bug into the test’s definition of correct behavior.
Make unresolved assumptions visible
Sometimes an input genuinely leaves more than one interpretation open. In that case, report the assumption the software selected and, where appropriate, its confidence. A clearly labeled uncertain result lets a person or downstream system decide whether to accept it, review it, or request more information.
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Choose checks that match the failure
- Wrong values: compare against independently known values or domain rules.
- Missing records: reconcile counts, totals, or other completeness checks where the input supplies them.
- Lost state or work: test the relevant lifecycle transitions and verify that saved changes persist.
- Freeze or ANR: inspect platform traces and thread state rather than treating a plausible output as the main symptom.
What does an Android ANR tell you?
Android Developers defines an application-not-responding (ANR) condition in terms of a UI thread blocked too long. For an input-dispatch ANR, the app’s main thread has not responded to an input event within the system’s timeout. Android’s documentation gives a default of five seconds for AOSP and Pixel devices, while warning that OEM timeout values can vary; five seconds is not a universal setting for every Android device.
ANR diagnosis helps investigate hangs and freezes. It does not establish whether an app’s completed calculations or displayed data are correct.
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Where to start diagnosing an ANR
- Find the cluster signature: start with the ANR cluster in Google Play Console or Firebase Crashlytics, as recommended in the Android Developers ANR guidance.
- Inspect thread activity: use Perfetto to see whether the app’s main thread is running, runnable, or blocked, and examine relevant
system_serverthreads for issues such as lock contention. - Check likely causes: Android’s guidance lists main-thread blocking, lock contention, blocking I/O, and expensive rendering among causes of input-dispatch ANRs.
The same Android guidance warns that violating view-threading rules can lead to silent UI freezes, crashes, or ANRs. View objects should be accessed on the thread that created the view hierarchy, usually the main/UI thread. The documentation also notes a synchronization-barrier leak fixed in Android 17; apps with view-threading violations can still experience symptoms on Android 16 and lower. These version-specific details are about Android UI behavior, not a general explanation for incorrect data.
Why an absence of crashes is not reassurance
Crash reports are useful for locating failures that terminate a process. They cannot, on their own, tell you whether a result that looked successful was complete or correct. For that, the program needs an oracle: a source of expected behavior independent of the implementation, such as a known fixture, a printed total, or an invariant the result must satisfy.
Where no reliable expected answer exists, software should avoid presenting an inference as certainty. Making assumptions and uncertainty visible is part of making the behavior inspectable, not a substitute for fixing defects.
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