Microsoft Technology Licensing, LLC

Mark Anthony Jelf Downie

Jackson Michael Davis

Andrew R Sterland

Thomas Lai

Del Myers

Gregory Bernard Miskelly

Nikolaus Lee Karpinsky

Joseph Mark Schmitt

Justin Michael Anderson

Claudiu-Constantin Guiman

Wai Hang (“Barry”) Tang

William Xie

Embodiments improve software defect diagnosis. Analytic focus is automatically walked back from an initial symptomatic diagnostic context to a previous diagnostic context that is closer to underlying causes. Diagnosis may obtain diagnostic artifacts such as traces or dumps, extract diagnostic context, decompile executables, lookup likely causes based on symptoms, scan logs, and submit diagnostic context to software analysis services. An analysis service may perform static analysis, security testing, symptom-pair lookups, or antipattern scanning, for example, and may include a neural network or other machine learning model, for example. Root causes are culled from analysis results and identified to a software developer. Changes to mitigate the defect's impact are suggested in some cases. Thus, the software developer receives debugging leads without manually navigating through all the tool interfaces or unrelated details of diagnostic contexts. This allows the developer to more efficiently reach a useful diagnosis of defects, even for unfamiliar issues.

Software diagnostic context selection and use

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.

Automatically identifying a diagnostic analyzer applicable to a diagnostic artifact

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Hilliard, OH, United States of America

Mark Anthony Jelf Downie