Rust provides an exciting combination of strong safety guarantees and high performance. Many new systems are being implemented in Rust. Nevertheless, there is a large body of existing C code that could greatly benefit from Rust's safety guarantees. Unfortunately, the manual effort required to rewrite C code into Rust is often prohibitively expensive. Researchers have explored tools to assist developers in trans-lating legacy C code into Rust code. However, the mismatch between C abstractions and idiomatic Rust abstractions makes it challenging to automatically utilize Rust's language features, resulting in non-idiomatic Rust code that requires extensive manual effort to further refactor. For example, existing tools often fail to map polymorphic uses of void pointers in C to Rust's generic pointers. In this paper, we present a translation tool, GenC2Rust, that translates non-generic C code into generic Rust code. GenC2Rust statically analyzes the use of void pointers in the C program to compute the typing constraints and then retypes the parametric polymorphic void pointers into generic pointers. We conducted an evaluation of GenC2Rust across 42 C programs that vary in size and span multiple domains to demonstrate its scalability as well as correctness. We discovered GenC2Rust has translated 4,572 void pointers to use generics. We also discuss the limiting factors encountered in the translation process.

Autonomous Vehicles (AVs) leverage advanced sensing and networking technologies (e.g., camera, LiDAR, RADAR, GPS, DSRC, 5G, etc.) to enable safe and efficient driving without human drivers. Although still in its infancy, AV technology is becoming increasingly common and could radically transform our transportation system and by extension, our economy and society. As a result, there is tremendous global enthusiasm for research, development, and deployment of AVs, e.g., self-driving taxis and trucks from Waymo and Baidu. The current practice for testing AVs uses virtual tests—where AVs are tested in software simulations—since they offer a more efficient and safer alternative compared to field operational tests. Specifically, search-based approaches are used to find particularly critical situations. These approaches provide an opportunity to automatically generate tests; however, systematically creatingvalidandeffectivetests for AV software remains a major challenge. To address this challenge, we introducescenoRITA, a test generation approach for AVs that uses an evolutionary algorithm with (1) a novel gene representation that allows obstacles to befully mutable, hence, resulting in more reported violations and more diverse scenarios, (2) 5 test oracles to determine both safety and motion sickness-inducing violations and (3) a novel technique to identify and eliminate duplicate tests. Our extensive evaluation shows thatscenoRITAcan produce test scenarios that are more effective in revealing ADS bugs and more diverse in covering different parts of the map compared to other state-of-the-art test generation approaches.