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The paper tests the performance of LLM for program repair. The same topic as Automated Program Repair in the Era of Large Pre-trained Language Models. Differently, this paper focuses more on the details, whose program repair setting is much more complicated.

Some conclusions were drawn:

• LLMs can generate fixes to bugs.
• But for real-world settings, the performance is not enough.

Background:

• Security bugs are significant.
• LLMs are popular and has outstanding performance.

Implementation:

RQ1: Can off-the-shelf LLMs generate safe and functional code to fix security vulnerabilities?

RQ2: Does varying the amount of context in the comments of a prompt affect the LLM’s ability to suggest fixes?

RQ3: What are the challenges when using LLMs to fix vulnerabilities in the real world?

RQ4: How reliable are LLMs at generating repairs?

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This paper performs the first in-depth investigation on the user-data disposal of used IoT devices, and finds that:

1. Most users lack the awareness of disposing used IoT devices.
2. IoT devices collect more sensitive data than users expect, and current data protections of used IoT devices are inadequate.
3. The disposal methods of used IoT devices are often ineffective.

Implementation:

RQ1: Which kinds of sensitive data reside in used IoT devices?

RQ2: Which methods can be used to dispose of sensitive data?

RQ3: Are existing disposal methods effective in erasing the sensitive data?