PF-Miner: A practical paired functions mining method for Android kernel in error paths

Liu, Hu-Qiu; Wang, Yu-Ping; Bai, Jia-Ju; Hu, Shi‐Min

doi:10.1016/j.jss.2016.02.007

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…By contrast, we are interested in "exists" rules in the sense that in any given context a particular type of event emitter emits one event from a certain set, but not necessarily all of them. WN-Miner [32] and PF-Miner [33] focus more narrowly on the problem of inferring temporal specifications, specifically pairs of functions f and g such that g must always be invoked after f , usually because it performs some sort of cleanup. Acharya et al [34] generalize this to inferring partial orders between functions.…”

Section: Related Workmentioning

confidence: 99%

Learning how to listen: Automatically finding bug patterns in event-driven JavaScript APIs

Arteca¹,

Schäfer²,

Tip³

2021

Preprint

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Event-driven programming is widely practiced in the JavaScript community, both on the client side to handle UI events and AJAX requests, and on the server side to accommodate long-running operations such as file or network I/O. Many popular event-based APIs allow event names to be specified as free-form strings without any validation, potentially leading to lost events for which no listener has been registered and dead listeners for events that are never emitted. In previous work, Madsen et al. presented a precise static analysis for detecting such problems, but their analysis does not scale because it may require a number of contexts that is exponential in the size of the program. Concentrating on the problem of detecting dead listeners, we present an approach to learn how to correctly use event-based APIs by first mining a large corpus of JavaScript code using a simple static analysis to identify code snippets that register an event listener, and then applying statistical modeling to identify unusual patterns, which often indicate incorrect API usage. From a large-scale evaluation on 127,531 open-source JavaScript code bases, our technique was able to detect 75 incorrect listener-registration patterns, while maintaining a precision of 90.9% and recall of 7.5% over our validation set, demonstrating that a learning-based approach to detecting event-handling bugs is feasible. In an additional experiment, we investigated instances of these patterns in 25 open-source projects, and reported 30 issues to the project maintainers, of which 7 have been confirmed as bugs.

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Section: Related Workmentioning

confidence: 99%

Learning how to listen: Automatically finding bug patterns in event-driven JavaScript APIs

Arteca¹,

Schäfer²,

Tip³

2021

Preprint

View full text Add to dashboard Cite

show abstract

Section: Static Analysis Of Error Handling Codementioning

confidence: 99%

“…Some approaches [8], [52]- [55] use static analysis to detect problems in error handling code. PF-Miner [52] identifies error handling paths in Android kernel source code, and then respectively collects function call sequences in normal execution paths and error handling paths. By using statistical analysis to compare the collected function call sequences in these two kinds of code paths, PF-Miner mines frequently used resource-acquiring and resource-release function pairs as API rules.…”

Section: Static Analysis Of Error Handling Codementioning

confidence: 99%

Fuzzing Error Handling Code in Device Drivers Based on Software Fault Injection

Jiang

Bai

Lawall

et al. 2019

2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)

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Device drivers remain a main source of runtime failures in operating systems. To detect bugs in device drivers, fuzzing has been commonly used in practice. However, a main limitation of existing fuzzing approaches is that they cannot effectively test error handling code. Indeed, these fuzzing approaches require effective inputs to cover target code, but much error handling code in drivers is triggered by occasional errors (such as insufficient memory and hardware malfunctions) that are not related to inputs. In this paper, based on software fault injection, we propose a new fuzzing approach named FIZZER, to test error handling code in device drivers. At compile time, FIZZER uses static analysis to recommend possible error sites that can trigger error handling code. During driver execution, by analyzing runtime information, it automatically fuzzes error-site sequences for fault injection to improve code coverage. We evaluate FIZZER on 18 device drivers in Linux 4.19, and in total find 22 real bugs. The code coverage is increased by over 15% compared to normal execution without fuzzing.

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“…We cannot directly apply this to the property access bug detection technique either, as property accesses do not have a required temporal relationship, and the existence of one property does not directly imply the existence of another. WN-Miner [187] and PF-Miner [91] focus more narrowly on the problem of inferring temporal specifications, specifically pairs of functions f and g such that g must always be invoked after f , usually because it performs some sort of cleanup. Acharya et al [4] generalize this to inferring partial orders between functions.…”

Section: Related Workmentioning

confidence: 99%

Leveraging large code bases for bug detection and test generation

Arteca

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PF-Miner: A practical paired functions mining method for Android kernel in error paths

Cited by 8 publications

References 2 publications

Learning how to listen: Automatically finding bug patterns in event-driven JavaScript APIs

Learning how to listen: Automatically finding bug patterns in event-driven JavaScript APIs

Fuzzing Error Handling Code in Device Drivers Based on Software Fault Injection

Leveraging large code bases for bug detection and test generation

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