Summary
"*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 in the must be the same as in the .""
DEBUGGING. System invariants allow narrowing down causes of software behavior (""The X.509 public key certificate is not recognized if 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 containing '""${jndi:}""' opens ""). Problematic queries can be isolated and investigated until the problem is fixed.
In the future, testing, debugging, and monitoring would thus be taken over by _software bots_ who would autonomously explore software behavior, report issues, and suggest actions to their human co-workers, boosting developer productivity and software reliability.
"
IT workers are expensive and scarce. So why can't we further automate boring, repetitive activities such as testing and debugging? The problem is that we lack computer-readable _specifications_ (so-called _oracles_) for what the system should do or not do. For decades, this _oracle problem_ has been a roadblock to automated test generation, trusted software repairs, and accurate monitoring of software.
Building on groundbreaking research to infer input languages of systems, S3 introduces a unified approach to _learning oracles automatically_. It takes a given software system; _infers_ and _decodes_ its inputs and outputs; and runs _experiments_ to extract _models_ of how the system behaves, capturing its semantics by predicting output features for given input features.
These models, named _system invariants_, allow to _fully automate_ critical software development activities:
TESTING. System invariants encode _languages_ for automatically generating test inputs and provide _oracles_ for checking test results: ""In the TLS server, the in the must be the same as in the .""
DEBUGGING. System invariants allow narrowing down causes of software behavior (""The X.509 public key certificate is not recognized if contains a zero byte""). Generated tests and oracles ensure reliable automated repair.
MONITORING. System invariants enable detecting abnormal behavior at runtime (""In 'log4j', logging a containing '""${jndi:}""' opens ""). 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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101093186 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2028 |
| Total budget - Public funding: | 2 500 000,00 Euro - 2 500 000,00 Euro |
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Original description
"*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 in the must be the same as in the .""
DEBUGGING. System invariants allow narrowing down causes of software behavior (""The X.509 public key certificate is not recognized if 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 containing '""${jndi:}""' opens ""). 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.
"
Status
SIGNEDCall topic
ERC-2022-ADGUpdate Date
12-03-2024
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