Ben-gurion University of the Negev Research and Development Authority

International Business Machines Corporation

Bar-ilan University

B.G. Negev Technologies and Applications Ltd.

Meir Kalech

Ron Zvi Stern

Lior Chen

Lior Rokach

Gal Kaminka

Dennis Potashnik

Amir Elmishali

Elihau Khalaschi

Eliahu Khalastchi

Raz Lin

Roni Stern

Netanel Hasidi

In an example computer-implemented method, a dataset and a query including an expression to be matched to the dataset is received via a processor. A false positive rate (FPR) and a false negative rate (FNR) is calculated via the processor for each possible value assignment of a plurality of possible value assignments in response to detecting a missing value in the dataset. A value assignment is selected, via the processor, from the plurality of possible value assignments based on the FPR and the FNR. A response to the query is generated via the processor based on the selected value assignment.

Generating responses to queries based on selected value assignments

A method for diagnosing and troubleshooting failures of components of a physical system with low troubleshooting cost, according to which for each component in the system, a Model-Based Diagnosis (MBD) is used for computing the probability of causing a system failure, based on currently observed system behavior or on knowledge about the system's structure. Then the probability of causing a system failure is computed, based on its age and its survival curves. Then, it is determined whether a faulty component C should be fixed or replaced by minimizing future troubleshooting costs, being the costs of the process of diagnosing and repairing an observed failure.

Combined model-based approach and data driven prediction for troubleshooting faults in physical systems

The present invention is directed to a method for diagnosing faults in a software system, according to which, predictions of the probability of each software component of the software system to be faulty are obtained from a diagnoser, and the probability predictions are input to a fault diagnosis software module, executed by the diagnoser.

Data-augmented software diagnosis method and a diagnoser therefor

An artificial intelligence based method for improving a software testing process, according to which upon finding a bug, a set of candidate diagnoses is proposed to the tester, based on a Model-Based Diagnosis (MBD) process. A planning process is used for automatically suggesting further test steps to be performed by the tester, to identify the correct diagnosis for the developer in the form of faulty software component that caused the bug, while minimizing the tests steps performed by the tester. Additional information is provided to the MBD process, based on the outputs of the further test steps, thereby pruning incorrect candidate diagnoses. These steps are iteratively repeated while in each time, minimizing the set of candidate diagnoses, until a single diagnosis remains in the set.

Using model-based diagnosis to improve software testing

A method for detecting and diagnosing sensor faults in an autonomous system that includes sensors and hardware components, according to which sensors are related to hardware components and correlations between data readings are recognized online and correlation between sensors is determined. Predefined suspicious patterns are identified by online and continuously tracking the data readings from each sensor and detecting correlation breaks over time. The readings from sensors that match at least one of the patterns are marked as uncertain. For each online reading of the sensors, whenever sensors that used to be correlated show a different behavior, reporting that the reading indicates a fault. Upon identifying fault detection, diagnosing which of the internal components or sensors caused the fault, based on a function that returns the state of the sensor which is associated with the fault detection.

Sensor fault detection and diagnosis for autonomous systems

A method for detecting an anomaly in operation of a data analysis device, comprising: receiving present real-time readings of multiple sensors associated with the data analysis device, and maintaining a history of past real-time readings; determining which of said multiple sensors are correlated; computing a deviation between at least some of said present and at least some of said past real-time readings of said correlated sensors; and declaring an anomaly when said deviation exceeds a predetermined threshold.

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Modiin, Israel

Meir Kalech