Detection of runtime errors using machine learning

Detection of runtime errors using machine learning

Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Wei, Yijin, Sivaraman, Kalpathy Sitaraman, Miller, Shaun, Zilouchian Moghaddam, Roshanak, Sundaresan, Neelakantan
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Beschreibung
Zusammenfassung:Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.