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While many real-world programs are shipped with configurations to enable/disable functionalities, fuzzers have mostly been applied to test single configurations of these programs. In this work, we first conduct an empirical study to understand how program configurations affect fuzzing performance. We find that limiting a campaign to a single configuration can result in failing to cover a significant amount of code. We also observe that different program configurations contribute differing amounts of code coverage, challenging the idea that each one can be efficiently fuzzed individually. Motivated by these two observations, we propose ConfigFuzz , which can fuzz configurations along with normal inputs. ConfigFuzz transforms the target program to encode its program options within part of the fuzzable input, so existing fuzzers’ mutation operators can be reused to fuzz program configurations. We instantiate ConfigFuzz on six configurable, common fuzzing targets, and integrate their executions in FuzzBench. In our evaluation, ConfigFuzz outperforms two baseline fuzzers in four targets, while the results are mixed in the other targets due to program size and configuration space. We also analyze the options fuzzed by ConfigFuzz and how they affect the performance. 

Quantum computing technology may soon deliver revolutionary improvements in algorithmic performance, but it is useful only if computed answers are correct. While hardware-level decoherence errors have garnered significant attention, a less recognized obstacle to correctness is that of human programming errors—“bugs.” Techniques familiar to most programmers from the classical domain for avoiding, discovering, and diagnosing bugs do not easily transfer, at scale, to the quantum domain because of its unique characteristics. To address this problem, we have been working to adapt formal methods to quantum programming. With such methods, a programmer writes a mathematical specification alongside the program and semiautomatically proves the program correct with respect to it. The proof’s validity is automatically confirmed—certified—by a “proof assistant.” Formal methods have successfully yielded high-assurance classical software artifacts, and the underlying technology has produced certified proofs of major mathematical theorems. As a demonstration of the feasibility of applying formal methods to quantum programming, we present a formally certified end-to-end implementation of Shor’s prime factorization algorithm, developed as part of a framework for applying the certified approach to general applications. By leveraging our framework, one can significantly reduce the effects of human errors and obtain a high-assurance implementation of large-scale quantum applications in a principled way. 

We introduce Qunity, a new quantum programming language designed to treat quantum computing as a natural generalization of classical computing. Qunity presents a unified syntax where familiar programming constructs can have both quantum and classical effects. For example, one can use sum types to implement the direct sum of linear operators, exception-handling syntax to implement projective measurements, and aliasing to induce entanglement. Further, Qunity takes advantage of the overlooked BQP subroutine theorem, allowing one to construct reversible subroutines from irreversible quantum algorithms through the uncomputation of "garbage" outputs. Unlike existing languages that enable quantum aspects with separate add-ons (like a classical language with quantum gates bolted on), Qunity provides a unified syntax and a novel denotational semantics that guarantees that programs are quantum mechanically valid. We present Qunity's syntax, type system, and denotational semantics, showing how it can cleanly express several quantum algorithms. We also detail how Qunity can be compiled into a low-level qubit circuit language like OpenQASM, proving the realizability of our design. 

Fuzz testing is an active area of research with proposed improvements published at a rapid pace. Such proposals are assessed empirically: Can they be shown to perform better than the status quo? Such an assessment requires a benchmark of target programs with well-identified, realistic bugs. To ease the construction of such a benchmark, this paper presents FIXREVERTER, a tool that automatically injects realistic bugs in a program. FIXREVERTER takes as input a bugfix pattern which contains both code syntax and semantic conditions. Any code site that matches the specified syntax is undone if the semantic conditions are satisfied, as checked by static analysis, thus (re)introducing a likely bug. This paper focuses on three bugfix patterns, which we call conditional-abort, conditional-execute, and conditional-assign, based on a study of fixes in a corpus of Common Vulnerabilities and Exposures (CVEs). Using FIXREVERTER we have built REVBUGBENCH, which consists of 10 programs into which we have injected nearly 8,000 bugs; the programs are taken from FuzzBench and Binutils, and represent common targets of fuzzing evaluations. We have integrated REVBUGBENCH into the FuzzBench service, and used it to evaluate five fuzzers. Fuzzing performance varies by fuzzer and program, as desired/expected. Overall, 219 unique bugs were reported, 19% of which were detected by just one fuzzer. 

We present a formal model of Checked C, a dialect of C that aims to enforce spatial memory safety. Our model pays particular attention to the semantics of dynamically sized, potentially null-terminated arrays. We formalize this model in Coq, and prove that any spatial memory safety errors can be blamed on portions of the program labeled unchecked; this is a Checked C feature that supports incremental porting and backward compatibility. While our model's operational semantics uses annotated (“fat”) pointers to enforce spatial safety, we show that such annotations can be safely erased. Using PLT Redex we formalize an executable version of our model and a compilation procedure to an untyped C-like language, as well as use randomized testing to validate that generated code faithfully simulates the original. Finally, we develop a custom random generator for well-typed and almost-well-typed terms in our Redex model, and use it to search for inconsistencies between our model and the Clang Checked C implementation. We find these steps to be a useful way to co-develop a language (Checked C is still in development) and a core model of it. 

Rust is a general-purpose programming language that is both type-and memory-safe. Rust does not use a garbage collector, but rather achieves these properties through a sophisticated, but complex, type system. Doing so makes Rust very efficient, but makes Rust relatively hard to learn and use. We designed Bronze, an optional, library-based garbage collector for Rust. To see whether Bronze could make Rust more usable, we conducted a randomized controlled trial with volunteers from a 633--person class, collecting data from 428 students in total. We found that for a task that required managing complex aliasing, Bronze users were more likely to complete the task in the time available, and those who did so required only about a third as much time (4 hours vs. 12 hours). We found no significant difference in total time, even though Bronze users re-did the task without Bronze afterward. Surveys indicated that ownership, borrowing, and lifetimes were primary causes of the challenges that users faced when using Rust. 

Programming languages such as Rust and Go were developed to combat common and potentially devastating memory safety-related vulnerabilities. But adoption of new, more secure languages can be fraught and complex. To better understand the benefits and challenges of adopting Rust in particular, we conducted semi-structured interviews with professional, primarily senior software developers who have worked with Rust on their teams or tried to introduce it (n = 16), and we deployed a survey to the Rust development community (n = 178). We asked participants about their personal experiences using Rust, as well as experiences using Rust at their companies. We find a range of positive features, including good tooling and documentation, benefits for the development lifecycle, and improvement of overall secure coding skills, as well as drawbacks including a steep learning curve, limited library support, and concerns about the ability to hire additional Rust developers in the future. Our results have implications for promoting the adoption of Rust specifically and secure programming languages and tools more generally. 

BullFrog is a relational DBMS that supports single-step schema migrations --- even those that are backwards incompatible --- without downtime, and without need for advanced warning. When a schema migration is submitted, BullFrog initiates a logical switch to the new schema, but physically migrates affected data lazily, as it is accessed by incoming transactions. BullFrog's internal concurrency control algorithms and data structures enable concurrent processing of schema migration operations with post-migration transactions, while ensuring exactly-once migration of all old data into the physical layout required by the new schema. BullFrog is implemented as an open source extension to PostgreSQL. Experiments using this prototype over a TPC-C based workload (supplemented to include schema migrations) show that BullFrog can achieve zero-downtime migration to non-trivial new schemas with near-invisible impact on transaction throughput and latency. 

As quantum computing progresses steadily from theory into practice, programmers will face a common problem: How can they be sure that their code does what they intend it to do? This paper presents encouraging results in the application of mechanized proof to the domain of quantum programming in the context of the SQIR development. It verifies the correctness of a range of a quantum algorithms including Grover’s algorithm and quantum phase estimation, a key component of Shor’s algorithm. In doing so, it aims to highlight both the successes and challenges of formal verification in the quantum context and motivate the theorem proving community to target quantum computing as an application domain.