# Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language Model

arXiv: https://arxiv.org/abs/2310.15657 · HTML: https://mlsanity.com/paper/2310.15657/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2023.

## Built on by (most cited first, 10 of 10)

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- [On the Challenges of Fuzzing Techniques via Large Language Models](https://mlsanity.com/paper/2402.00350/) (2024)
- [WebTestPilot: Agentic End-to-End Web Testing against Natural Language Specification by Inferring Oracles with Symbolized GUI Elements](https://mlsanity.com/paper/2602.11724/) (2026)
- [Generative transformations and patterns in LLM-native approaches for software verification and falsification](https://mlsanity.com/paper/2404.09384/) (2024)
- [Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach](https://mlsanity.com/paper/2412.06684/) (2024)
- [LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models](https://mlsanity.com/paper/2510.10179/) (2025)
- [Can LLMs Test Terminal User Interfaces?](https://mlsanity.com/paper/2608.03743/) (2026)

## Abstract (arXiv)

Mobile applications have become a ubiquitous part of our daily life, providing users with access to various services and utilities. Text input, as an important interaction channel between users and applications, plays an important role in core functionality such as search queries, authentication, messaging, etc. However, certain special text (e.g., -18 for Font Size) can cause the app to crash, and generating diversified unusual inputs for fully testing the app is highly demanded. Nevertheless, this is also challenging due to the combination of explosion dilemma, high context sensitivity, and complex constraint relations. This paper proposes InputBlaster which leverages the LLM to automatically generate unusual text inputs for mobile app crash detection. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. In detail, InputBlaster leverages LLM to produce the test generators together with the mutation rules serving as the reasoning chain, and utilizes the in-context learning schema to demonstrate the LLM with examples for boosting the performance. InputBlaster is evaluated on 36 text input widgets with cash bugs involving 31 popular Android apps, and results show that it achieves 78% bug detection rate, with 136% higher than the best baseline. Besides, we integrate it with the automated GUI testing tool and detect 37 unseen crashes in real-world apps from Google Play.