Juniper Networks, Inc.

Plume Design, Inc.

Sachin Vasudeva

Yusuke Sakamoto

Abhishek Kumar Singh

Nipun Agarwal

Tao Deng

Muhammad Ali Valliani

Yoseph Malkin

Srinibas Maharana

Manu J Prakash

Hong Zeng

Systems, methods, and non-transitory computer-readable storage media are provided for predicting the likelihood or probability of a subscriber of a service to cancel or not renew a subscription. A method, according to one implementation, includes a step of receiving data pertaining to aspects of a service that is provided by a service provider to a subscriber in accordance with a subscription. The data may include one or more impact factors each having a positive, neutral, or negative influence on the likelihood of subscriber churn. The method also includes a step of using the one or more impact factors to predict the likelihood that the subscriber will cancel the subscription.

Predicting the likelihood of subscriber churn

Intelligent monitoring systems to predict likelihood of subscriber churn

System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for the network condition of each of the plurality of customers of one or more service providers; determining an internet service provider (ISP) outage based on a plurality of factors; and performing one of a plurality of resolution or notification actions.

Intelligent monitoring systems and methods for Wi-Fi metric-based ISP outage detection for cloud based Wi-Fi networks

A server device is configured to receive a request to identify a manner in which changed code propagates within an application; generate a group of blocks that correspond to code associated with a parent function corresponding to the application and which includes the changed code; perform an intra-procedural analysis on the group of blocks to identify a block that is affected by the changed code included within an epicenter block; perform an inter-procedural analysis on functions associated with the block, where, when performing the inter-procedural analysis, the server device is to generate another group of blocks associated with the functions, and identify another block that is affected by the changed code included within the epicenter block; and present, for display, information associated with the block or the other block that enables the application to be tested based on the block or the other block.

Automated parallel software code impact analysis

Static analysis of software code may be performed in a selective regression testing framework. The static impact analysis, when carried out in a comprehensive and efficient way, can help make the test selection safer and more complete. One method for performing static analysis in selective regression testing of a software project includes, for example, storing changes to source code that defines functions and global data elements of the software project; performing a static analysis of the source code to determine which global data elements in the source code correspond to changed functions in the source code and executing a regression test of the software project using a set of test units determined based, at least in part, on the static analysis.

Static analysis in selective software regression testing

In general, techniques are described for intelligently selecting test cases within testing environments. Specifically, a computing device may include a user interface with which a user interacts to specify a safety level. The device also includes a control unit that determines those functions of source code that changed. Based on these changed functions, the control unit determines those functions of the source code not changed but impacted by the changes within a set distance. The control unit then selects, for each of these determined functions, one or more test cases in accordance with one or more test selection algorithms by adaptively applying these algorithms in order of decreasing safety assurances and increasing precision. The control unit further aggregates these selected test cases and implement the test cases to ensure the safety level with respect to a percentage of functional errors occurring within a software program compiled from the source code.

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Fremont, CA, United States of America

Sachin Vasudeva