Statistical Software Properties: Definition, Inference and Monitoring

Software properties define how software systems should operate. Specifying correct properties, however, can be difficult and expensive as it requires deep knowledge of the system\u27s expected behavior and the environment in which it operates. Automated analysis techniques to infer properties from code or code executions can mitigate that cost, but are still unable to go beyond state properties and the simplest patterns of temporal properties. This limitation renders properties that sacrifice fault detection power. To address this problem, we introduce a new type of software properties called \textit{statistical properties}, which characterize significant statistical relationships among the values of variables across program states. We define an approach to infer these relationships automatically and support their monitoring while controlling the trade-offs between overhead and the precision and recall of the inferred properties. We perform several experiments to assess the approach in the context of distributed robotics applications. Our findings indicate that the inferred statistical properties can be use to generate precise and cost-effective models capable of detecting faults in software systems while keeping the number of false positives close to zero and previous knowledge of the software system design and behavior unnecessary. Adviser: Sebastian Elbau

Statistical Software Properties: Definition, Inference and Monitoring

Software properties define how software systems should operate. Specifying correct properties, however, can be difficult and expensive as it requires deep knowledge of the system\u27s expected behavior and the environment in which it operates. Automated analysis techniques to infer properties from code or code executions can mitigate that cost, but are still unable to go beyond state properties and the simplest patterns of temporal properties. This limitation renders properties that sacrifice fault detection power. To address this problem, we introduce a new type of software properties called \textit{statistical properties}, which characterize significant statistical relationships among the values of variables across program states. We define an approach to infer these relationships automatically and support their monitoring while controlling the trade-offs between overhead and the precision and recall of the inferred properties. We perform several experiments to assess the approach in the context of distributed robotics applications. Our findings indicate that the inferred statistical properties can be use to generate precise and cost-effective models capable of detecting faults in software systems while keeping the number of false positives close to zero and previous knowledge of the software system design and behavior unnecessary. Adviser: Sebastian Elbau