Using entropy measures for comparison of software traces

TL;DR

This paper introduces entropy-based measures, including Shannon and extended entropies, as scalable and effective tools for classifying and comparing software execution traces, especially in defect analysis.

Contribution

It proposes a novel approach using entropy measures for software trace comparison, demonstrating their efficiency and superiority over traditional methods.

Findings

Extended entropies outperform Shannon entropy in trace classification.

Entropy measures effectively distinguish software traces related to different defects.

The method is scalable and suitable for large software trace datasets.

Abstract

The analysis of execution paths (also known as software traces) collected from a given software product can help in a number of areas including software testing, software maintenance and program comprehension. The lack of a scalable matching algorithm operating on detailed execution paths motivates the search for an alternative solution. This paper proposes the use of word entropies for the classification of software traces. Using a well-studied defective software as an example, we investigate the application of both Shannon and extended entropies (Landsberg-Vedral, R\'{e}nyi and Tsallis) to the classification of traces related to various software defects. Our study shows that using entropy measures for comparisons gives an efficient and scalable method for comparing traces. The three extended entropies, with parameters chosen to emphasize rare events, all perform similarly and are superior to the Shannon entropy.