Among the many software testing techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.

The Art, Science, and Engineering of Fuzzing: A Survey

Among the many software vulnerability discovery techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective

The injection of faults has been widely used to evaluate fault tolerance mechanisms and to assess the impact of faults in computer systems. However, the injection of software faults is not as well understood as other classes of faults (e.g., hardware faults). In this paper, we analyze how software faults can be injected (emulated) in a source-code independent manner. We specifically address important emulation requirements such as fault representativeness and emulation accuracy. We start with the analysis of an extensive collection of real software faults. We observed that a large percentage of faults falls into well-defined classes and can be characterized in a very precise way, allowing accurate emulation of software faults through a small set of emulation operators. A new software fault injection technique (G-SWFIT) based on emulation operators derived from the field study is proposed. This technique consists of finding key programming structures at the machine code-level where high-level software faults can be emulated. The fault-emulation accuracy of this technique is shown. This work also includes a study on the key aspects that may impact the technique accuracy. The portability of the technique is also discussed and it is shown that a high degree of portability can be achieved

Emulation of Software Faults: A Field Data Study and a Practical Approach

Mission-critical system designers may have to use a commercial off-the-shelf (COTS) approach to reduce costs and shorten development time, even though COTS software components may not specifically be designed for robust operation. Automated testing can assess component robustness without sacrificing the advantages of a COTS approach. This paper describes the Ballista methodology for scalable, portable, automated robustness testing of component interfaces. An object-oriented approach based on parameter data types rather than component functionality essentially eliminates the need for function-specific test scaffolding. A full-scale implementation that automatically tests the robustness of 233 operating system software components has been ported to ten POSIX systems. Between 42% and 63% of components tested had robustness problems, with a normalized failure rate ranging from 10% to 23% of tests conducted. Robustness testing could be used by developers to measure and improve robustness, or by consumers to compare the robustness of competing COTS component libraries.

/pdf/automated-robustness-testing-of-off-the-shelf-software-5c78g6t9o3.pdf

Automated robustness testing of off-the-shelf software components

Critical system designers are turning to off-the-shelf operating system (OS) software to reduce costs and time-to-marker. Unfortunately general-purpose OSes do not always respond to exceptional conditions robustly, either accepting exceptional values without complaint, or suffering abnormal task termination. Even though direct measurement is impractical, this paper uses a multiversion comparison technique to reveal a 6% to 19% normalized rate at which exceptional parameter values cause no error report in commercial POSIX OS implementations. Additionally, 168 functions across 13 OSes are compared to reveal common mode robustness failures. While the best single OS has a 12.6% robustness failure rare for system calls, 3.8% of failures are common across all 13 OSes examined. However, combining C library calls with system calls increases these rates to 29.5% for the best single OS and 17.0% for common mode failures. These results suggest that OS implementations are not completely diverse, and that C library junctions are both less diverse and less robust than system calls.

Comparing the robustness of POSIX operating systems

When creating mission-critical distributed systems using off-the-shelf components, it is important to assess the dependability of not only the hardware, but the software as well. This paper proposes a way to test operating system dependability. The concept of response regions is presented as a way to visualize erroneous system behavior and gain insight into failure mechanisms. A 5-point "CRASH" (catastrophic, restart, abort, silent, hindering) scale is defined for grading the severity of robustness vulnerabilities encountered. Test results from five operating systems are analyzed for robustness vulnerabilities, and exhibit a range of dependability. Robustness benchmarking comparisons of this type may provide important information to both users and designers of off-the-shelf software for dependable systems.

/pdf/comparing-operating-systems-using-robustness-benchmarks-21g300i3hv.pdf

Comparing operating systems using robustness benchmarks

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Comparing operating systems using robustness benchmarks