Automatically identifying critical input regions and code in applications

@inproceedings{Carbin2010AutomaticallyIC,
  title={Automatically identifying critical input regions and code in applications},
  author={Michael Carbin and Martin C. Rinard},
  booktitle={ISSTA '10},
  year={2010}
}
Applications that process complex inputs often react in different ways to changes in different regions of the input. Small changes to forgiving regions induce correspondingly small changes in the behavior and output. Small changes to critical regions, on the other hand, can induce disproportionally large changes in the behavior or output. Identifying the critical and forgiving regions in the input and the corresponding critical and forgiving regions of code is directly relevant to many software… 
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References

SHOWING 1-10 OF 34 REFERENCES
Taint-based directed whitebox fuzzing
TLDR
The results indicate that the new directed fuzzing technique can effectively expose errors located deep within large programs, especially appropriate for testing programs that have complex, highly structured input file formats.
Penumbra: automatically identifying failure-relevant inputs using dynamic tainting
TLDR
The results are promising, as they show that PENUMBRA can point developers to inputs that are actually relevant for investigating a failure and can be more practical than existing alternative approaches.
Automatically patching errors in deployed software
TLDR
Aspects of ClearView that make it particularly appropriate for this context include its ability to generate patches without human intervention, apply and remove patchesto and from running applications without requiring restarts or otherwise perturbing the execution, and identify and discard ineffective or damaging patches by evaluating the continued behavior of patched applications.
Feedback-Directed Random Test Generation
TLDR
Experimental results indicate that feedback-directed random test generation can outperform systematic and undirectedrandom test generation, in terms of coverage and error detection.
Prospex: Protocol Specification Extraction
TLDR
This paper presents a system that is capable of automatically inferring state machines, and introduces techniques for identifying and clustering different types of messages not only based on their structure, but also according to the impact of each message on server behavior.
Flayer: Exposing Application Internals
TLDR
This paper presents an effective fault injection testing technique and an automation library, LibFlayer, which explores techniques for vulnerability patch analysis and guided source code auditing.
Tupni: automatic reverse engineering of input formats
TLDR
Tupni is presented, a tool that can reverse engineer an input format with a rich set of information, including record sequences, record types, and input constraints, and can generalize the format specification over multiple inputs.
Automatic Protocol Format Reverse Engineering through Context-Aware Monitored Execution
TLDR
The results show that AutoFormat can not only identify individual message fields automatically and with high accuracy, but also unveil the structure of the protocol format by revealing possible relations among the message fields.
An empirical study of the reliability of UNIX utilities
The following section describes the tools we built to test the utilities. These tools include the fuzz (random character) generator, ptyjig (to test interactive utilities), and scripts to automate…
Dynamic Taint Analysis for Automatic Detection, Analysis, and SignatureGeneration of Exploits on Commodity Software
Software vulnerabilities have had a devastating effect on the Internet. Worms such as CodeRed and Slammer can compromise hundreds of thousands of hosts within hours or even minutes, and cause…
...
1
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3
4
...