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Harrison Green, Claire Le Goues, Fraser Brown
The software status quo is to use one system to process many different kinds of inputs. In contrast, we propose hyperspecialization: creating new software that is optimized for a single class of inputs. Hyperspecializing manually is anywhere from expensive to impossible. We …
Harrison Green, Claire Le Goues, Fraser Brown
The software status quo is to use one system to process many different kinds of inputs. In contrast, we propose hyperspecialization: creating new software that is optimized for a single class of inputs. Hyperspecializing manually is anywhere from expensive to impossible. We …
us (code pays fourni par la source)
Harrison Green, Claire Le Goues, Fraser Brown
Coverage-guided fuzzers are powerful automated bug-finding tools. They mutate program inputs, observe coverage, and save any input that hits an unexplored path for future mutation. Unfortunately, without knowledge of input formats—for example, the relationship between formats’ data fields and sizes—fuzzers are prone …
us (code pays fourni par la source)
Harrison Green, Fraser Brown, Claire Le Goues
Fuzzing is a powerful technique for finding bugs in software libraries, but scaling it remains difficult. Automated harness generation commits to fixed API sequences at synthesis time, limiting the behaviors each harness can test. Approaches that instead explore new sequences dynamically lack …
Harrison Green, Fraser Brown, Claire Le Goues
Fuzzing is a powerful technique for finding bugs in software libraries, but scaling it remains difficult. Automated harness generation commits to fixed API sequences at synthesis time, limiting the behaviors each harness can test. Approaches that instead explore new sequences dynamically lack …
Xiang Li, Siyu Lu, Sarro Federica, Claire Le Goues et autres
Automated software environment setup is a prerequisite for testing, debugging, and reproducing failures, yet remains challenging in practice due to complex dependencies, heterogeneous build systems, and incomplete documentation. Recent work leverages large language models to automate this process, but typically evaluates success …
Xiang Li, Siyu Lu, Sarro Federica, Claire Le Goues et autres
Automated software environment setup is a prerequisite for testing, debugging, and reproducing failures, yet remains challenging in practice due to complex dependencies, heterogeneous build systems, and incomplete documentation. Recent work leverages large language models to automate this process, but typically evaluates success …
Daniel Ramos, Catarina Gamboa, Inês Lynce, Vasco Manquinho et autres
Library migration is a common but error-prone task in software development. Developers may need to replace one library with another due to reasons like changing requirements or licensing changes. Migration typically entails updating and rewriting source code manually. While automated migration tools …
Daniel Ramos, Catarina Gamboa, Inês Lynce, Vasco Manquinho et autres
Library migration is a common but error-prone task in software development. Developers may need to replace one library with another due to reasons like changing requirements or licensing changes. Migration typically entails updating and rewriting source code manually. While automated migration tools …
Massimiliano Di Penta, Kelly Coyle Blincoe, M. Chechik, Claire Le Goues et autres
As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, …
nz, us, ca, sg (code pays fourni par la source)
Massimiliano Di Penta, Kelly Coyle Blincoe, M. Chechik, Claire Le Goues et autres
As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, …
Luke Dramko, Claire Le Goues, Edward J. Schwartz
Neural decompilers are machine learning models that reconstruct the source code from an executable program. Critical to the lifecycle of any machine learning model is an evaluation of its effectiveness. However, existing techniques for evaluating neural decompilation models are generally inadequate, especially …
us (code pays fourni par la source)
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