Detection Software Content Failures Using Dynamic Execution Information

TL;DR

This paper introduces a novel method for detecting software content failures at runtime by transforming dynamic execution data into a measure called runtime entropy and applying machine learning classification, improving failure detection in complex systems.

Contribution

The paper presents a new failure detection indicator based on runtime entropy from dynamic execution data, combined with machine learning, to identify software content failures.

Findings

Effective classification of intended vs. unintended behaviors.

Feasibility demonstrated through experiments on open source projects.

Machine learning models achieved high accuracy in failure detection.

Abstract

Modern software systems become too complex to be tested and validated. Detecting software partial failures in complex systems at runtime assist to handle software unintended behaviors, avoiding catastrophic software failures and improving software runtime availability. These detection techniques aim to find the manifestation of faults before they finally lead to unavoidable failures, thus supporting following runtime fault tolerant techniques. We review the state of the art articles and find that the content failures account for the majority of all kinds of software failures, but its detection methods are rarely studied. In this work, we propose a novel failure detection indicator based on the software runtime dynamic execution information for software content failures. The runtime information is recorded during software execution, then transformed to a measure named runtime entropy and finally fed into machine learning models. The machine learning models are built to classify the intended and unintended behaviors of the objected software systems. A series of controlled experiments on several open source projects are conducted to prove the feasibility of the method. We also evaluate the accuracy of machine learning models built in this work.