17526176. AUTOMATICALLY IDENTIFYING A DIAGNOSTIC ANALYZER APPLICABLE TO A DIAGNOSTIC ARTIFACT simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

AUTOMATICALLY IDENTIFYING A DIAGNOSTIC ANALYZER APPLICABLE TO A DIAGNOSTIC ARTIFACT

Organization Name

MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor(s)

Del Myers of Seattle WA (US)

William Xie of Sammamish WA (US)

Mark Anthony Jelf Downie of Hilliard OH (US)

Joseph Mark Schmitt of Seattle WA (US)

Justin Michael Anderson of Dublin CA (US)

Gregory Bernard Miskelly of Seattle WA (US)

Claudiu-Constantin Guiman of Brooklyn NY (US)

AUTOMATICALLY IDENTIFYING A DIAGNOSTIC ANALYZER APPLICABLE TO A DIAGNOSTIC ARTIFACT - A simplified explanation of the abstract

This abstract first appeared for US patent application 17526176 titled 'AUTOMATICALLY IDENTIFYING A DIAGNOSTIC ANALYZER APPLICABLE TO A DIAGNOSTIC ARTIFACT

Simplified Explanation

Methods, systems, and computer program products are described for automatically identifying a diagnostic analyzer that applies to a diagnostic artifact. Here are the key points:

• The invention involves a plurality of diagnostic analyzers that analyze diagnostic artifacts related to previous software executions.
• A confidence measure is calculated for each diagnostic analyzer, indicating the likelihood that it applies to a specific diagnostic artifact.
• The confidence measure is determined by applying specific heuristics to the diagnostic artifact, with the outcome of each heuristic contributing to the confidence measure.
• Based on the calculated confidence measures and predetermined thresholds, it is determined whether to include each diagnostic analyzer in a set of analyzers for analyzing the diagnostic artifact.

Potential applications of this technology:

• Software debugging: The invention can be used to automatically identify the most relevant diagnostic analyzer for a given software artifact, aiding in the debugging process.
• Quality assurance: By accurately identifying the appropriate diagnostic analyzer, this technology can help improve the quality assurance process for software development.
• Performance optimization: The invention can assist in identifying the diagnostic analyzer that can provide insights into performance issues, allowing for more efficient optimization.

Problems solved by this technology:

• Manual selection of diagnostic analyzers: The invention automates the process of selecting the most suitable diagnostic analyzer, saving time and effort for software developers.
• Inaccurate analysis: By using heuristics and confidence measures, the invention ensures that the diagnostic analyzer chosen is the most relevant for the diagnostic artifact, reducing false positives and false negatives.
• Scalability: With a large number of diagnostic analyzers available, this technology efficiently determines the appropriate analyzers to include in the analysis set, ensuring scalability.

Benefits of this technology:

• Improved efficiency: By automatically selecting the most relevant diagnostic analyzer, this technology speeds up the diagnostic and debugging process, leading to faster software development cycles.
• Enhanced accuracy: The use of confidence measures and heuristics ensures that the chosen diagnostic analyzer is the most appropriate, reducing the chances of incorrect analysis.
• Cost savings: By automating the selection process, this technology reduces the need for manual intervention, saving time and resources for software development teams.

Original Abstract Submitted

Methods, systems, and computer program products for using a confidence measure to automatically identify a diagnostic analyzer that applies to a diagnostic artifact. A plurality of diagnostic analyzers are each configured to analyze diagnostic artifacts relating to prior executions of software entities. A confidence measure is calculated for each diagnostic analyzer. Each confidence measure indicates a likelihood that the diagnostic analyzer applies to a particular diagnostic artifact. Calculating each confidence measure comprises applying one or more heuristics specific to the diagnostic analyzer against the particular diagnostic artifact, with an outcome of application of each heuristic contributing to the confidence measure for the respective diagnostic analyzer. Based at least on calculating the confidence measure for each diagnostic analyzer, and based on one or more determined thresholds, it is determined whether to include each diagnostic analyzers in a set of diagnostic analyzers with which to analyze the particular diagnostic artifact.