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

TL;DR

This paper presents an integrated approach combining logical and probabilistic reasoning to improve software debugging by accurately identifying likely sources of errors in complex programs.

Contribution

It introduces a novel method that merges logical and uncertain reasoning techniques for diagnosing software problems, enhancing debugging effectiveness.

Findings

Preliminary results show improved error localization accuracy.

The approach guides engineers to high-likelihood error paths.

Combining reasoning methods aids in complex software debugging.

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.