Paulo Canelas

• PhD Student at Carnegie Mellon University

Large Language Models (LLMs) have become integral to various software engineering tasks, including code generation, bug detection, and repair. To evaluate model performance in these domains, numerous bug benchmarks containing real-world bugs from software projects have been developed. However, a growing concern within the software engineering commu...

The Robot Operating System (ROS) is a popular framework and ecosystem that allows developers to build robot software systems from reusable, off-the-shelf components. Systems are often built by customizing and connecting components via configuration files. While reusable components theoretically allow rapid prototyping, ensuring proper configuration...

Developers want to detect bugs as early in the development lifecycle as possible, as the effort and cost to fix them increases with the incremental development of features. Ultimately, bugs that are only found in production can have catastrophic consequences. Type systems are effective at detecting many classes of bugs during development, often pro...

Genetic Programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generation, Program Synthesis and Improvement of Software (GI). Grammar-Guided Genetic Programming (GGGP) approaches allow the user to refin...

Search-Based Software Engineering problems frequently have semantic constraints that can be used to deterministically restrict what type of programs can be generated, improving the performance of Genetic Programming. Strongly-Typed and Grammar-Guided Genetic Programming are two examples of using domain-knowledge to improve performance of Genetic Pr...

Bugs that are detected earlier during the development lifecycle are easier and cheaper to fix, whereas bugs that are found during production are difficult and expensive to address, and may have dire consequences. Type systems are particularly effective at identifying and preventing bugs early in the development lifecycle by causing invalid programs...

Automated test generation helps programmers to test their software with minimal intervention. Automated test generation tools produce a set of program inputs that maximize the possible execution paths, presented as a test coverage metric. Proposed approaches fall within three main approaches. Search-based methods work on any program by randomly sea...

The performance of Evolutionary Algorithms is frequently hindered by arbitrarily large search spaces. In order to overcome this challenge, domain-specific knowledge is often used to restrict the representation or evaluation of candidate solutions to the problem at hand. Due to the diversity of problems and the unpredictable performance impact, the...