S3
Financiado
Semantics of Software Systems
"*What if we had software bots that tirelessly test, debug, and monitor our software systems?*
IT workers are expensive and scarce. So why can't we further automate boring, repetitive activities such as testing and debugging? The...
"*What if we had software bots that tirelessly test, debug, and monitor our software systems?*
IT workers are expensive and scarce. So why can't we further automate boring, repetitive activities such as testing and debugging? The problem is that we lack computer-readable _specifications_ (so-called _oracles_) for what the system should do or not do. For decades, this _oracle problem_ has been a roadblock to automated test generation, trusted software repairs, and accurate monitoring of software.
Building on groundbreaking research to infer input languages of systems, S3 introduces a unified approach to _learning oracles automatically_. It takes a given software system; _infers_ and _decodes_ its inputs and outputs; and runs _experiments_ to extract _models_ of how the system behaves, capturing its semantics by predicting output features for given input features.
These models, named _system invariants_, allow to _fully automate_ critical software development activities:
TESTING. System invariants encode _languages_ for automatically generating test inputs and provide _oracles_ for checking test results: ""In the TLS server, the <payload> in the <heartbeat-response> must be the same as in the <heartbeat-request>.""
DEBUGGING. System invariants allow narrowing down causes of software behavior (""The X.509 public key certificate is not recognized if <subject-name> contains a zero byte""). Generated tests and oracles ensure reliable automated repair.
MONITORING. System invariants enable detecting abnormal behavior at runtime (""In 'log4j', logging a <user-agent> containing '""${jndi:<url>}""' opens <url>""). Problematic queries can be isolated and investigated until the problem is fixed.
In the future, testing, debugging, and monitoring would thus be taken over by _software bots_ who would autonomously explore software behavior, report issues, and suggest actions to their human co-workers, boosting developer productivity and software reliability.
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Duración del proyecto: 60 meses
Fecha Inicio: 2023-08-22
Fecha Fin: 2028-08-31
Fecha Fin: 2028-08-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-08-22
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2022-ADG:
ERC ADVANCED GRANTS
Cerrada
hace 2 años
Presupuesto
El presupuesto total del proyecto asciende a
3M€
Líder del proyecto
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Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5