A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging

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📝 Original Info

  • Title: A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging
  • ArXiv ID: 1303.1488
  • Date: 2013-03-08
  • Authors: Researchers from original ArXiv paper

📝 Abstract

We describe the integration of logical and uncertain reasoning methods to identify the likely source and location of software problems. To date, software engineers have had few tools for identifying the sources of error in complex software packages. We describe a method for diagnosing software problems through combining logical and uncertain reasoning analyses. Our preliminary results suggest that such methods can be of value in directing the attention of software engineers to paths of an algorithm that have the highest likelihood of harboring a programming error.

💡 Deep Analysis

Deep Dive into A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging.

We describe the integration of logical and uncertain reasoning methods to identify the likely source and location of software problems. To date, software engineers have had few tools for identifying the sources of error in complex software packages. We describe a method for diagnosing software problems through combining logical and uncertain reasoning analyses. Our preliminary results suggest that such methods can be of value in directing the attention of software engineers to paths of an algorithm that have the highest likelihood of harboring a programming error.

📄 Full Content

We describe the integration of logical and uncertain reasoning methods to identify the likely source and location of software problems. To date, software engineers have had few tools for identifying the sources of error in complex software packages. We describe a method for diagnosing software problems through combining logical and uncertain reasoning analyses. Our preliminary results suggest that such methods can be of value in directing the attention of software engineers to paths of an algorithm that have the highest likelihood of harboring a programming error.

Reference

This content is AI-processed based on ArXiv data.

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