Avaya Inc.

John D Palframan

Randy L Hackbarth

Audris Mockus

Juan Jenny Li

James M Landwehr

Brett Alan Shockley

Disclosed herein are systems, methods, and computer-readable storage media for identifying and remediating risky source files. An example system configured to practice the method can gather data describing each file in a source code repository, and generate, using a weighted algorithm based on empirical relationships between the data and customer-found defects, a risk score for each file in the source code repository, wherein the weighted algorithm prioritizes factors based on predictiveness of defects. Then the system can generate a list of files having risk scores above a threshold, and make risk-mitigation recommendations based on the risk scores. A file can include a single file or a collection of files such as a module. The system can identify, for each file in the list of files having risk scores above the threshold, a respective risk type, and make the risk-mitigation recommendation for each file based on the respective risk type.

System and method for prioritizing and remediating defect risk in source code

Systems, methods, and computer-readable storage media for predicting escalation risks in customer support contexts. A system implementing the example method can identify instances of customer escalation from a corpus of customer support sales, customer support, and product software development data, and determine, for the instances of customer escalation, sets of trigger conditions that were at least partially responsible for respective instances of customer escalation. Then the system can classify each of the instances of customer escalation by problem type based on the set of trigger conditions to yield classifications, and add the classifications, the sets of trigger conditions, and the instances of customer escalation to a database. Then the system can compare the database to support data to identify customers or support tasks that are likely to be escalated, and the system or a user can allocate additional support resources to proactively prevent escalations.

System and method for prioritizing customers and predicting service escalation

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for analyzing source code and identifying potential defects. The methods employ both static analysis and dynamic testing to detect program defects early in the development stage for better quality with less cost. The analysis also ranks identified potential defects and reports only the most likely defects to a human developer. Once defects are detected, they can be removed right away and similar defects can be prevented automatically.

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Middletown, NJ, United States of America

John D Palframan