Combining Dynamic Symbolic Execution, Machine Learning and Search-Based Testing to Automatically Generate Test Cases for Classes

TL;DR

This paper introduces a novel approach that combines dynamic symbolic execution, machine learning, and search-based testing to automatically generate comprehensive test cases for object-oriented classes, addressing limitations of existing methods.

Contribution

It presents a new integrated technique that leverages the strengths of multiple testing methods to improve test case generation for classes.

Findings

Preliminary experiments show promising results.

The combined approach is more thorough than individual techniques.

Initial data supports effectiveness of the method.

Abstract

This article discusses a new technique to automatically generate test cases for object oriented programs. At the state of the art, the problem of generating adequate sets of complete test cases has not been satisfactorily solved yet. There are various techniques to automatically generate test cases (random testing, search-based testing, etc.) but each one has its own weaknesses. This article proposes an approach that distinctively combines dynamic symbolic execution, search-based testing and machine learning, to efficiently generate thorough class-level test suites. The preliminary data obtained carrying out some experiments confirm that we are going in the right direction.